detach("package:dplyr", unload = TRUE)
library(dplyr)

# read in data
wide <- read.csv(file = "peril_data_deid.csv", header = TRUE) 
head(wide)
##    X reliability sex  subj agem experiment exp     cost video_quality
## 1 NA          NA   m  S1_1 10.5 LUTS.Exp.1     Barriers            NA
## 2 NA          NA   f S1_10  9.9 LUTS.Exp.1     Barriers            NA
## 3 NA          NA   m S1_11  9.8 LUTS.Exp.1     Barriers            NA
## 4 NA          NA   m S1_12  9.9 LUTS.Exp.1     Barriers            NA
## 5 NA          NA   f S1_13 10.2 LUTS.Exp.1     Barriers            NA
## 6 NA          NA   f S1_14 10.0 LUTS.Exp.1     Barriers            NA
##   audio_quality device highchair HV_side first_test first_fam
## 1            NA               NA    left         HV        HL
## 2            NA               NA    left         LV        LH
## 3            NA               NA    left         HV        HL
## 4            NA               NA   right         LV        LH
## 5            NA               NA   right         HV        LH
## 6            NA               NA   right         LV        HL
##   first_test_deeper_side control_deeper_side control_firstevent control_1
## 1                                                                        
## 2                                                                        
## 3                                                                        
## 4                                                                        
## 5                                                                        
## 6                                                                        
##   control_2 fam1  fam2  fam3  fam4  fam5  fam6 test1 test2 test3 test4 avg_fam
## 1             60    60    60    60 59.12  9.75  32.8  6.38  14.2    60      51
## 2             60    60 50.06 23.11 13.93 14.26 54.04 20.82 11.82 19.46      37
## 3             60    60 48.73    60 29.89 36.65 52.47 18.15 34.18 30.82      49
## 4             60    60    60 37.15 13.48 51.68 33.26 45.95  5.65 14.07      47
## 5             60    60    60 40.67 28.57 10.77  7.22  6.86   6.5  <NA>      43
## 6             60 11.14 56.06    60 32.97 28.77 17.37  9.95 12.22  6.71      41
##   sum_fam testavg_lower testavg_higher lower1 lower2 higher1 higher2
## 1     309          33.2           23.5   6.38     60    32.8    14.2
## 2     221          32.9           20.1  54.04  11.82   20.82   19.46
## 3     295          24.5           43.3  18.15  30.82   52.47   34.18
## 4     282          19.5           30.0  33.26   5.65   45.95   14.07
## 5     260           6.9            6.9   6.86   <NA>    7.22     6.5
## 6     249          14.8            8.3  17.37  12.22    9.95    6.71
##   control_shallow control_deep
## 1                             
## 2                             
## 3                             
## 4                             
## 5                             
## 6
str(wide)
## 'data.frame':    286 obs. of  40 variables:
##  $ X                     : int  NA NA NA NA NA NA NA NA NA NA ...
##  $ reliability           : int  NA NA NA NA NA NA NA NA NA NA ...
##  $ sex                   : chr  "m" "f" "m" "m" ...
##  $ subj                  : chr  "S1_1" "S1_10" "S1_11" "S1_12" ...
##  $ agem                  : num  10.5 9.9 9.8 9.9 10.2 ...
##  $ experiment            : chr  "LUTS.Exp.1" "LUTS.Exp.1" "LUTS.Exp.1" "LUTS.Exp.1" ...
##  $ exp                   : chr  "" "" "" "" ...
##  $ cost                  : chr  "Barriers" "Barriers" "Barriers" "Barriers" ...
##  $ video_quality         : int  NA NA NA NA NA NA NA NA NA NA ...
##  $ audio_quality         : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ device                : chr  "" "" "" "" ...
##  $ highchair             : int  NA NA NA NA NA NA NA NA NA NA ...
##  $ HV_side               : chr  "left" "left" "left" "right" ...
##  $ first_test            : chr  "HV" "LV" "HV" "LV" ...
##  $ first_fam             : chr  "HL" "LH" "HL" "LH" ...
##  $ first_test_deeper_side: chr  "" "" "" "" ...
##  $ control_deeper_side   : chr  "" "" "" "" ...
##  $ control_firstevent    : chr  "" "" "" "" ...
##  $ control_1             : chr  "" "" "" "" ...
##  $ control_2             : chr  "" "" "" "" ...
##  $ fam1                  : chr  "60" "60" "60" "60" ...
##  $ fam2                  : chr  "60" "60" "60" "60" ...
##  $ fam3                  : chr  "60" "50.06" "48.73" "60" ...
##  $ fam4                  : chr  "60" "23.11" "60" "37.15" ...
##  $ fam5                  : chr  "59.12" "13.93" "29.89" "13.48" ...
##  $ fam6                  : chr  "9.75" "14.26" "36.65" "51.68" ...
##  $ test1                 : chr  "32.8" "54.04" "52.47" "33.26" ...
##  $ test2                 : chr  "6.38" "20.82" "18.15" "45.95" ...
##  $ test3                 : chr  "14.2" "11.82" "34.18" "5.65" ...
##  $ test4                 : chr  "60" "19.46" "30.82" "14.07" ...
##  $ avg_fam               : num  51.5 36.9 49.2 47 43.3 ...
##  $ sum_fam               : num  309 221 295 282 260 ...
##  $ testavg_lower         : num  33.19 32.93 24.49 19.46 6.86 ...
##  $ testavg_higher        : num  23.5 20.14 43.33 30.01 6.86 ...
##  $ lower1                : chr  "6.38" "54.04" "18.15" "33.26" ...
##  $ lower2                : chr  "60" "11.82" "30.82" "5.65" ...
##  $ higher1               : chr  "32.8" "20.82" "52.47" "45.95" ...
##  $ higher2               : chr  "14.2" "19.46" "34.18" "14.07" ...
##  $ control_shallow       : chr  "" "" "" "" ...
##  $ control_deep          : chr  "" "" "" "" ...
wide.info <- wide %>% filter(exp == "Exp.1b"|
                             exp == "Exp.2b"|
                             exp == "Exp.3b") 
table(wide.info$sex)
## 
##  f  m 
## 52 50
# convert into long format
long <- gather(wide, type, look, testavg_lower:control_deep) 
str(long)
## 'data.frame':    2288 obs. of  34 variables:
##  $ X                     : int  NA NA NA NA NA NA NA NA NA NA ...
##  $ reliability           : int  NA NA NA NA NA NA NA NA NA NA ...
##  $ sex                   : chr  "m" "f" "m" "m" ...
##  $ subj                  : chr  "S1_1" "S1_10" "S1_11" "S1_12" ...
##  $ agem                  : num  10.5 9.9 9.8 9.9 10.2 ...
##  $ experiment            : chr  "LUTS.Exp.1" "LUTS.Exp.1" "LUTS.Exp.1" "LUTS.Exp.1" ...
##  $ exp                   : chr  "" "" "" "" ...
##  $ cost                  : chr  "Barriers" "Barriers" "Barriers" "Barriers" ...
##  $ video_quality         : int  NA NA NA NA NA NA NA NA NA NA ...
##  $ audio_quality         : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ device                : chr  "" "" "" "" ...
##  $ highchair             : int  NA NA NA NA NA NA NA NA NA NA ...
##  $ HV_side               : chr  "left" "left" "left" "right" ...
##  $ first_test            : chr  "HV" "LV" "HV" "LV" ...
##  $ first_fam             : chr  "HL" "LH" "HL" "LH" ...
##  $ first_test_deeper_side: chr  "" "" "" "" ...
##  $ control_deeper_side   : chr  "" "" "" "" ...
##  $ control_firstevent    : chr  "" "" "" "" ...
##  $ control_1             : chr  "" "" "" "" ...
##  $ control_2             : chr  "" "" "" "" ...
##  $ fam1                  : chr  "60" "60" "60" "60" ...
##  $ fam2                  : chr  "60" "60" "60" "60" ...
##  $ fam3                  : chr  "60" "50.06" "48.73" "60" ...
##  $ fam4                  : chr  "60" "23.11" "60" "37.15" ...
##  $ fam5                  : chr  "59.12" "13.93" "29.89" "13.48" ...
##  $ fam6                  : chr  "9.75" "14.26" "36.65" "51.68" ...
##  $ test1                 : chr  "32.8" "54.04" "52.47" "33.26" ...
##  $ test2                 : chr  "6.38" "20.82" "18.15" "45.95" ...
##  $ test3                 : chr  "14.2" "11.82" "34.18" "5.65" ...
##  $ test4                 : chr  "60" "19.46" "30.82" "14.07" ...
##  $ avg_fam               : num  51.5 36.9 49.2 47 43.3 ...
##  $ sum_fam               : num  309 221 295 282 260 ...
##  $ type                  : chr  "testavg_lower" "testavg_lower" "testavg_lower" "testavg_lower" ...
##  $ look                  : chr  "33.19" "32.93" "24.49" "19.46" ...
# log transform looks
long$look <- as.numeric(as.character(long$look))
long$loglook <- log(long$look)

# set levels for different kinds of looks
long$type <- factor(long$type)

# subset averaged looks across test pairs (2 observations per participant) and control events
long.avg <- long %>% 
  filter(type == "testavg_higher" | type == "testavg_lower" | type == "control_deep" | type =="control_shallow") %>%
  separate(type, into=c("phase", "type"), sep="_")%>%
  # add age groups (relevant for Experiments 1-3)
  mutate(agegroup = as.factor(case_when(agem < 12 ~ "younger",
                            agem > 12 ~ "older")))

long.avg$type <- factor(long.avg$type)
long.avg$phase <- factor(long.avg$phase)
long.avg$exp <- factor(long.avg$exp)
long.avg$sex <- relevel(as.factor(long.avg$sex), ref = "m")

exp1.avg <-dplyr::filter(long.avg, exp == "Exp.1")
exp2.avg <-dplyr::filter(long.avg, exp == "Exp.2")
exp3.avg <-dplyr::filter(long.avg, exp == "Exp.3")
exp1b.avg <-dplyr::filter(long.avg, exp == "Exp.1b")
exp2b.avg <-dplyr::filter(long.avg, exp == "Exp.2b")
exp3b.avg <-dplyr::filter(long.avg, exp == "Exp.3b")

tenm <- rbind(exp1b.avg, exp2b.avg, exp3b.avg)
tenm 
##      X reliability sex   subj agem experiment    exp cost video_quality
## 1   NA           1   f   S5_1 10.4     RISK10 Exp.1b Risk            NA
## 2   NA           1   f   S5_2 10.4     RISK10 Exp.1b Risk            NA
## 3   NA           1   f   S5_3 10.3     RISK10 Exp.1b Risk            NA
## 4   NA           1   m   S5_4 10.4     RISK10 Exp.1b Risk            NA
## 5   NA           0   m   S5_5 10.1     RISK10 Exp.1b Risk            NA
## 6   NA           0   f   S5_6 10.5     RISK10 Exp.1b Risk            NA
## 7   NA           1   f   S5_7 10.1     RISK10 Exp.1b Risk            NA
## 8   NA           0   f   S5_8  9.8     RISK10 Exp.1b Risk            NA
## 9   NA           1   f   S5_9 10.3     RISK10 Exp.1b Risk            NA
## 10  NA           1   m  S5_10  9.9     RISK10 Exp.1b Risk            NA
## 11  NA           1   m  S5_11 10.6     RISK10 Exp.1b Risk            NA
## 12  NA           1   f  S5_12 10.1     RISK10 Exp.1b Risk            NA
## 13  NA           0   m  S5_13  9.8     RISK10 Exp.1b Risk            NA
## 14  NA           0   f  S5_14 10.6     RISK10 Exp.1b Risk            NA
## 15  NA           0   f  S5_15 10.2     RISK10 Exp.1b Risk            NA
## 16  NA           0   f  S5_16  9.6     RISK10 Exp.1b Risk            NA
## 17  NA           0   m  S5_17 10.0     RISK10 Exp.1b Risk            NA
## 18  NA           0   m  S5_18 10.2     RISK10 Exp.1b Risk            NA
## 19  NA           1   m  S5_19  9.8     RISK10 Exp.1b Risk            NA
## 20  NA           1   f  S5_20 10.4     RISK10 Exp.1b Risk            NA
## 21  NA           0   m  S5_21  9.6     RISK10 Exp.1b Risk            NA
## 22  NA           0   m  S5_22 10.5     RISK10 Exp.1b Risk            NA
## 23  NA           0   m  S5_23  9.6     RISK10 Exp.1b Risk            NA
## 24  NA           0   m  S5_24 10.1     RISK10 Exp.1b Risk            NA
## 25  NA           0   m  S5_25 10.2     RISK10 Exp.1b Risk            NA
## 26  NA           0   m  S5_26 10.0     RISK10 Exp.1b Risk            NA
## 27  NA           0   m  S5_27 10.3     RISK10 Exp.1b Risk            NA
## 28  NA           0   f  S5_28  9.7     RISK10 Exp.1b Risk            NA
## 29  NA           1   f  S5_29 10.0     RISK10 Exp.1b Risk            NA
## 30  NA           1   m  S5_30 10.4     RISK10 Exp.1b Risk            NA
## 31  NA           1   f  S5_31  9.8     RISK10 Exp.1b Risk            NA
## 32  NA           1   m  S5_32 10.6     RISK10 Exp.1b Risk            NA
## 33  NA           1   f   S5_1 10.4     RISK10 Exp.1b Risk            NA
## 34  NA           1   f   S5_2 10.4     RISK10 Exp.1b Risk            NA
## 35  NA           1   f   S5_3 10.3     RISK10 Exp.1b Risk            NA
## 36  NA           1   m   S5_4 10.4     RISK10 Exp.1b Risk            NA
## 37  NA           0   m   S5_5 10.1     RISK10 Exp.1b Risk            NA
## 38  NA           0   f   S5_6 10.5     RISK10 Exp.1b Risk            NA
## 39  NA           1   f   S5_7 10.1     RISK10 Exp.1b Risk            NA
## 40  NA           0   f   S5_8  9.8     RISK10 Exp.1b Risk            NA
## 41  NA           1   f   S5_9 10.3     RISK10 Exp.1b Risk            NA
## 42  NA           1   m  S5_10  9.9     RISK10 Exp.1b Risk            NA
## 43  NA           1   m  S5_11 10.6     RISK10 Exp.1b Risk            NA
## 44  NA           1   f  S5_12 10.1     RISK10 Exp.1b Risk            NA
## 45  NA           0   m  S5_13  9.8     RISK10 Exp.1b Risk            NA
## 46  NA           0   f  S5_14 10.6     RISK10 Exp.1b Risk            NA
## 47  NA           0   f  S5_15 10.2     RISK10 Exp.1b Risk            NA
## 48  NA           0   f  S5_16  9.6     RISK10 Exp.1b Risk            NA
## 49  NA           0   m  S5_17 10.0     RISK10 Exp.1b Risk            NA
## 50  NA           0   m  S5_18 10.2     RISK10 Exp.1b Risk            NA
## 51  NA           1   m  S5_19  9.8     RISK10 Exp.1b Risk            NA
## 52  NA           1   f  S5_20 10.4     RISK10 Exp.1b Risk            NA
## 53  NA           0   m  S5_21  9.6     RISK10 Exp.1b Risk            NA
## 54  NA           0   m  S5_22 10.5     RISK10 Exp.1b Risk            NA
## 55  NA           0   m  S5_23  9.6     RISK10 Exp.1b Risk            NA
## 56  NA           0   m  S5_24 10.1     RISK10 Exp.1b Risk            NA
## 57  NA           0   m  S5_25 10.2     RISK10 Exp.1b Risk            NA
## 58  NA           0   m  S5_26 10.0     RISK10 Exp.1b Risk            NA
## 59  NA           0   m  S5_27 10.3     RISK10 Exp.1b Risk            NA
## 60  NA           0   f  S5_28  9.7     RISK10 Exp.1b Risk            NA
## 61  NA           1   f  S5_29 10.0     RISK10 Exp.1b Risk            NA
## 62  NA           1   m  S5_30 10.4     RISK10 Exp.1b Risk            NA
## 63  NA           1   f  S5_31  9.8     RISK10 Exp.1b Risk            NA
## 64  NA           1   m  S5_32 10.6     RISK10 Exp.1b Risk            NA
## 65  NA           1   f   S5_1 10.4     RISK10 Exp.1b Risk            NA
## 66  NA           1   f   S5_2 10.4     RISK10 Exp.1b Risk            NA
## 67  NA           1   f   S5_3 10.3     RISK10 Exp.1b Risk            NA
## 68  NA           1   m   S5_4 10.4     RISK10 Exp.1b Risk            NA
## 69  NA           0   m   S5_5 10.1     RISK10 Exp.1b Risk            NA
## 70  NA           0   f   S5_6 10.5     RISK10 Exp.1b Risk            NA
## 71  NA           1   f   S5_7 10.1     RISK10 Exp.1b Risk            NA
## 72  NA           0   f   S5_8  9.8     RISK10 Exp.1b Risk            NA
## 73  NA           1   f   S5_9 10.3     RISK10 Exp.1b Risk            NA
## 74  NA           1   m  S5_10  9.9     RISK10 Exp.1b Risk            NA
## 75  NA           1   m  S5_11 10.6     RISK10 Exp.1b Risk            NA
## 76  NA           1   f  S5_12 10.1     RISK10 Exp.1b Risk            NA
## 77  NA           0   m  S5_13  9.8     RISK10 Exp.1b Risk            NA
## 78  NA           0   f  S5_14 10.6     RISK10 Exp.1b Risk            NA
## 79  NA           0   f  S5_15 10.2     RISK10 Exp.1b Risk            NA
## 80  NA           0   f  S5_16  9.6     RISK10 Exp.1b Risk            NA
## 81  NA           0   m  S5_17 10.0     RISK10 Exp.1b Risk            NA
## 82  NA           0   m  S5_18 10.2     RISK10 Exp.1b Risk            NA
## 83  NA           1   m  S5_19  9.8     RISK10 Exp.1b Risk            NA
## 84  NA           1   f  S5_20 10.4     RISK10 Exp.1b Risk            NA
## 85  NA           0   m  S5_21  9.6     RISK10 Exp.1b Risk            NA
## 86  NA           0   m  S5_22 10.5     RISK10 Exp.1b Risk            NA
## 87  NA           0   m  S5_23  9.6     RISK10 Exp.1b Risk            NA
## 88  NA           0   m  S5_24 10.1     RISK10 Exp.1b Risk            NA
## 89  NA           0   m  S5_25 10.2     RISK10 Exp.1b Risk            NA
## 90  NA           0   m  S5_26 10.0     RISK10 Exp.1b Risk            NA
## 91  NA           0   m  S5_27 10.3     RISK10 Exp.1b Risk            NA
## 92  NA           0   f  S5_28  9.7     RISK10 Exp.1b Risk            NA
## 93  NA           1   f  S5_29 10.0     RISK10 Exp.1b Risk            NA
## 94  NA           1   m  S5_30 10.4     RISK10 Exp.1b Risk            NA
## 95  NA           1   f  S5_31  9.8     RISK10 Exp.1b Risk            NA
## 96  NA           1   m  S5_32 10.6     RISK10 Exp.1b Risk            NA
## 97  NA           1   f   S5_1 10.4     RISK10 Exp.1b Risk            NA
## 98  NA           1   f   S5_2 10.4     RISK10 Exp.1b Risk            NA
## 99  NA           1   f   S5_3 10.3     RISK10 Exp.1b Risk            NA
## 100 NA           1   m   S5_4 10.4     RISK10 Exp.1b Risk            NA
## 101 NA           0   m   S5_5 10.1     RISK10 Exp.1b Risk            NA
## 102 NA           0   f   S5_6 10.5     RISK10 Exp.1b Risk            NA
## 103 NA           1   f   S5_7 10.1     RISK10 Exp.1b Risk            NA
## 104 NA           0   f   S5_8  9.8     RISK10 Exp.1b Risk            NA
## 105 NA           1   f   S5_9 10.3     RISK10 Exp.1b Risk            NA
## 106 NA           1   m  S5_10  9.9     RISK10 Exp.1b Risk            NA
## 107 NA           1   m  S5_11 10.6     RISK10 Exp.1b Risk            NA
## 108 NA           1   f  S5_12 10.1     RISK10 Exp.1b Risk            NA
## 109 NA           0   m  S5_13  9.8     RISK10 Exp.1b Risk            NA
## 110 NA           0   f  S5_14 10.6     RISK10 Exp.1b Risk            NA
## 111 NA           0   f  S5_15 10.2     RISK10 Exp.1b Risk            NA
## 112 NA           0   f  S5_16  9.6     RISK10 Exp.1b Risk            NA
## 113 NA           0   m  S5_17 10.0     RISK10 Exp.1b Risk            NA
## 114 NA           0   m  S5_18 10.2     RISK10 Exp.1b Risk            NA
## 115 NA           1   m  S5_19  9.8     RISK10 Exp.1b Risk            NA
## 116 NA           1   f  S5_20 10.4     RISK10 Exp.1b Risk            NA
## 117 NA           0   m  S5_21  9.6     RISK10 Exp.1b Risk            NA
## 118 NA           0   m  S5_22 10.5     RISK10 Exp.1b Risk            NA
## 119 NA           0   m  S5_23  9.6     RISK10 Exp.1b Risk            NA
## 120 NA           0   m  S5_24 10.1     RISK10 Exp.1b Risk            NA
## 121 NA           0   m  S5_25 10.2     RISK10 Exp.1b Risk            NA
## 122 NA           0   m  S5_26 10.0     RISK10 Exp.1b Risk            NA
## 123 NA           0   m  S5_27 10.3     RISK10 Exp.1b Risk            NA
## 124 NA           0   f  S5_28  9.7     RISK10 Exp.1b Risk            NA
## 125 NA           1   f  S5_29 10.0     RISK10 Exp.1b Risk            NA
## 126 NA           1   m  S5_30 10.4     RISK10 Exp.1b Risk            NA
## 127 NA           1   f  S5_31  9.8     RISK10 Exp.1b Risk            NA
## 128 NA           1   m  S5_32 10.6     RISK10 Exp.1b Risk            NA
## 129 NA           1   f  01-MR 10.1       MR10 Exp.2b Risk            NA
## 130 NA           0   m  03-MR 10.1       MR10 Exp.2b Risk            NA
## 131 NA           1   f  12-MR 10.3       MR10 Exp.2b Risk            NA
## 132 NA           0   f  13-MR 10.1       MR10 Exp.2b Risk            NA
## 133 NA           0   m  15-MR  9.8       MR10 Exp.2b Risk            NA
## 134 NA           0   f  17-MR 10.3       MR10 Exp.2b Risk            NA
## 135 NA           0   m  18-MR  9.7       MR10 Exp.2b Risk            NA
## 136 NA           1   m  19-MR 10.3       MR10 Exp.2b Risk            NA
## 137 NA           1   m  21-MR  9.5       MR10 Exp.2b Risk            NA
## 138 NA           0   f  22-MR  9.5       MR10 Exp.2b Risk            NA
## 139 NA           0   f  28-MR 10.3       MR10 Exp.2b Risk            NA
## 140 NA           1   f  29-MR  9.8       MR10 Exp.2b Risk            NA
## 141 NA           0   f  30-MR  9.6       MR10 Exp.2b Risk            NA
## 142 NA           1   f  35-MR 10.0       MR10 Exp.2b Risk            NA
## 143 NA           0   f  36-MR  9.7       MR10 Exp.2b Risk            NA
## 144 NA           0   f  37-MR 10.1       MR10 Exp.2b Risk            NA
## 145 NA           0   m  38-MR  9.0       MR10 Exp.2b Risk            NA
## 146 NA           1   f  41-MR  9.9       MR10 Exp.2b Risk            NA
## 147 NA           1   m  42-MR  9.9       MR10 Exp.2b Risk            NA
## 148 NA           0   f  43-MR  9.8       MR10 Exp.2b Risk            NA
## 149 NA           0   f  53-MR 10.3       MR10 Exp.2b Risk            NA
## 150 NA           0   f  55-MR 10.2       MR10 Exp.2b Risk            NA
## 151 NA           1   m  58-MR  9.5       MR10 Exp.2b Risk            NA
## 152 NA           1   f  60-MR 10.5       MR10 Exp.2b Risk            NA
## 153 NA           1   f  61-MR  9.6       MR10 Exp.2b Risk            NA
## 154 NA           0   m  62-MR 10.3       MR10 Exp.2b Risk            NA
## 155 NA           0   m  63-MR 10.4       MR10 Exp.2b Risk            NA
## 156 NA           1   m  64-MR 10.3       MR10 Exp.2b Risk            NA
## 157 NA           0   m  65-MR  9.9       MR10 Exp.2b Risk            NA
## 158 NA           1   m  66-MR 10.1       MR10 Exp.2b Risk            NA
## 159 NA           1   f  01-MR 10.1       MR10 Exp.2b Risk            NA
## 160 NA           0   m  03-MR 10.1       MR10 Exp.2b Risk            NA
## 161 NA           1   f  12-MR 10.3       MR10 Exp.2b Risk            NA
## 162 NA           0   f  13-MR 10.1       MR10 Exp.2b Risk            NA
## 163 NA           0   m  15-MR  9.8       MR10 Exp.2b Risk            NA
## 164 NA           0   f  17-MR 10.3       MR10 Exp.2b Risk            NA
## 165 NA           0   m  18-MR  9.7       MR10 Exp.2b Risk            NA
## 166 NA           1   m  19-MR 10.3       MR10 Exp.2b Risk            NA
## 167 NA           1   m  21-MR  9.5       MR10 Exp.2b Risk            NA
## 168 NA           0   f  22-MR  9.5       MR10 Exp.2b Risk            NA
## 169 NA           0   f  28-MR 10.3       MR10 Exp.2b Risk            NA
## 170 NA           1   f  29-MR  9.8       MR10 Exp.2b Risk            NA
## 171 NA           0   f  30-MR  9.6       MR10 Exp.2b Risk            NA
## 172 NA           1   f  35-MR 10.0       MR10 Exp.2b Risk            NA
## 173 NA           0   f  36-MR  9.7       MR10 Exp.2b Risk            NA
## 174 NA           0   f  37-MR 10.1       MR10 Exp.2b Risk            NA
## 175 NA           0   m  38-MR  9.0       MR10 Exp.2b Risk            NA
## 176 NA           1   f  41-MR  9.9       MR10 Exp.2b Risk            NA
## 177 NA           1   m  42-MR  9.9       MR10 Exp.2b Risk            NA
## 178 NA           0   f  43-MR  9.8       MR10 Exp.2b Risk            NA
## 179 NA           0   f  53-MR 10.3       MR10 Exp.2b Risk            NA
## 180 NA           0   f  55-MR 10.2       MR10 Exp.2b Risk            NA
## 181 NA           1   m  58-MR  9.5       MR10 Exp.2b Risk            NA
## 182 NA           1   f  60-MR 10.5       MR10 Exp.2b Risk            NA
## 183 NA           1   f  61-MR  9.6       MR10 Exp.2b Risk            NA
## 184 NA           0   m  62-MR 10.3       MR10 Exp.2b Risk            NA
## 185 NA           0   m  63-MR 10.4       MR10 Exp.2b Risk            NA
## 186 NA           1   m  64-MR 10.3       MR10 Exp.2b Risk            NA
## 187 NA           0   m  65-MR  9.9       MR10 Exp.2b Risk            NA
## 188 NA           1   m  66-MR 10.1       MR10 Exp.2b Risk            NA
## 189 NA           1   f  01-MR 10.1       MR10 Exp.2b Risk            NA
## 190 NA           0   m  03-MR 10.1       MR10 Exp.2b Risk            NA
## 191 NA           1   f  12-MR 10.3       MR10 Exp.2b Risk            NA
## 192 NA           0   f  13-MR 10.1       MR10 Exp.2b Risk            NA
## 193 NA           0   m  15-MR  9.8       MR10 Exp.2b Risk            NA
## 194 NA           0   f  17-MR 10.3       MR10 Exp.2b Risk            NA
## 195 NA           0   m  18-MR  9.7       MR10 Exp.2b Risk            NA
## 196 NA           1   m  19-MR 10.3       MR10 Exp.2b Risk            NA
## 197 NA           1   m  21-MR  9.5       MR10 Exp.2b Risk            NA
## 198 NA           0   f  22-MR  9.5       MR10 Exp.2b Risk            NA
## 199 NA           0   f  28-MR 10.3       MR10 Exp.2b Risk            NA
## 200 NA           1   f  29-MR  9.8       MR10 Exp.2b Risk            NA
## 201 NA           0   f  30-MR  9.6       MR10 Exp.2b Risk            NA
## 202 NA           1   f  35-MR 10.0       MR10 Exp.2b Risk            NA
## 203 NA           0   f  36-MR  9.7       MR10 Exp.2b Risk            NA
## 204 NA           0   f  37-MR 10.1       MR10 Exp.2b Risk            NA
## 205 NA           0   m  38-MR  9.0       MR10 Exp.2b Risk            NA
## 206 NA           1   f  41-MR  9.9       MR10 Exp.2b Risk            NA
## 207 NA           1   m  42-MR  9.9       MR10 Exp.2b Risk            NA
## 208 NA           0   f  43-MR  9.8       MR10 Exp.2b Risk            NA
## 209 NA           0   f  53-MR 10.3       MR10 Exp.2b Risk            NA
## 210 NA           0   f  55-MR 10.2       MR10 Exp.2b Risk            NA
## 211 NA           1   m  58-MR  9.5       MR10 Exp.2b Risk            NA
## 212 NA           1   f  60-MR 10.5       MR10 Exp.2b Risk            NA
## 213 NA           1   f  61-MR  9.6       MR10 Exp.2b Risk            NA
## 214 NA           0   m  62-MR 10.3       MR10 Exp.2b Risk            NA
## 215 NA           0   m  63-MR 10.4       MR10 Exp.2b Risk            NA
## 216 NA           1   m  64-MR 10.3       MR10 Exp.2b Risk            NA
## 217 NA           0   m  65-MR  9.9       MR10 Exp.2b Risk            NA
## 218 NA           1   m  66-MR 10.1       MR10 Exp.2b Risk            NA
## 219 NA           1   f  01-MR 10.1       MR10 Exp.2b Risk            NA
## 220 NA           0   m  03-MR 10.1       MR10 Exp.2b Risk            NA
## 221 NA           1   f  12-MR 10.3       MR10 Exp.2b Risk            NA
## 222 NA           0   f  13-MR 10.1       MR10 Exp.2b Risk            NA
## 223 NA           0   m  15-MR  9.8       MR10 Exp.2b Risk            NA
## 224 NA           0   f  17-MR 10.3       MR10 Exp.2b Risk            NA
## 225 NA           0   m  18-MR  9.7       MR10 Exp.2b Risk            NA
## 226 NA           1   m  19-MR 10.3       MR10 Exp.2b Risk            NA
## 227 NA           1   m  21-MR  9.5       MR10 Exp.2b Risk            NA
## 228 NA           0   f  22-MR  9.5       MR10 Exp.2b Risk            NA
## 229 NA           0   f  28-MR 10.3       MR10 Exp.2b Risk            NA
## 230 NA           1   f  29-MR  9.8       MR10 Exp.2b Risk            NA
## 231 NA           0   f  30-MR  9.6       MR10 Exp.2b Risk            NA
## 232 NA           1   f  35-MR 10.0       MR10 Exp.2b Risk            NA
## 233 NA           0   f  36-MR  9.7       MR10 Exp.2b Risk            NA
## 234 NA           0   f  37-MR 10.1       MR10 Exp.2b Risk            NA
## 235 NA           0   m  38-MR  9.0       MR10 Exp.2b Risk            NA
## 236 NA           1   f  41-MR  9.9       MR10 Exp.2b Risk            NA
## 237 NA           1   m  42-MR  9.9       MR10 Exp.2b Risk            NA
## 238 NA           0   f  43-MR  9.8       MR10 Exp.2b Risk            NA
## 239 NA           0   f  53-MR 10.3       MR10 Exp.2b Risk            NA
## 240 NA           0   f  55-MR 10.2       MR10 Exp.2b Risk            NA
## 241 NA           1   m  58-MR  9.5       MR10 Exp.2b Risk            NA
## 242 NA           1   f  60-MR 10.5       MR10 Exp.2b Risk            NA
## 243 NA           1   f  61-MR  9.6       MR10 Exp.2b Risk            NA
## 244 NA           0   m  62-MR 10.3       MR10 Exp.2b Risk            NA
## 245 NA           0   m  63-MR 10.4       MR10 Exp.2b Risk            NA
## 246 NA           1   m  64-MR 10.3       MR10 Exp.2b Risk            NA
## 247 NA           0   m  65-MR  9.9       MR10 Exp.2b Risk            NA
## 248 NA           1   m  66-MR 10.1       MR10 Exp.2b Risk            NA
## 249  1           1   m  10m_1  9.5        MR3 Exp.3b Risk             5
## 250  5           1   m  10m_5 10.4        MR3 Exp.3b Risk             5
## 251  9           1   m  10m_9  9.8        MR3 Exp.3b Risk             5
## 252 13           0   m 10m_13 10.1        MR3 Exp.3b Risk             5
## 253 17           0   m 10m_17  9.8        MR3 Exp.3b Risk             4
## 254 21           0   f 10m_21 10.7        MR3 Exp.3b Risk             5
## 255 25           0   f 10m_25 10.6        MR3 Exp.3b Risk             5
## 256 29           0   f 10m_29  9.9        MR3 Exp.3b Risk             5
## 257 33           0   f 10m_33  9.8        MR3 Exp.3b Risk             5
## 258 37           1   f 10m_37 10.2        MR3 Exp.3b Risk             5
## 259  2           0   m  10m_2 10.0        MR3 Exp.3b Risk             5
## 260  6           0   m  10m_6 10.7        MR3 Exp.3b Risk             4
## 261 10           0   m 10m_10 10.7        MR3 Exp.3b Risk             5
## 262 14           1   m 10m_14 10.7        MR3 Exp.3b Risk             5
## 263 22           1   f 10m_22 10.3        MR3 Exp.3b Risk             5
## 264 26           1   f 10m_26 11.1        MR3 Exp.3b Risk             5
## 265 30           1   f 10m_30 10.4        MR3 Exp.3b Risk             5
## 266 34           1   f 10m_34  9.8        MR3 Exp.3b Risk             5
## 267 38           1   f 10m_38 10.7        MR3 Exp.3b Risk             4
## 268  3           1   m  10m_3 10.4        MR3 Exp.3b Risk             5
## 269  7           1   m  10m_7 11.0        MR3 Exp.3b Risk             4
## 270 11           0   m 10m_11 10.1        MR3 Exp.3b Risk             5
## 271 15           0   m 10m_15 10.2        MR3 Exp.3b Risk             5
## 272 18           1   f 10m_18 10.2        MR3 Exp.3b Risk             5
## 273 19           1   m 10m_19 10.3        MR3 Exp.3b Risk             5
## 274 23           0   f 10m_23 10.3        MR3 Exp.3b Risk             5
## 275 27           0   f 10m_27 10.2        MR3 Exp.3b Risk             5
## 276 31           1   f 10m_31  9.9        MR3 Exp.3b Risk             4
## 277 35           0   f 10m_35 10.1        MR3 Exp.3b Risk             5
## 278 39           0   f 10m_39 10.0        MR3 Exp.3b Risk             5
## 279  4           0   m  10m_4 10.0        MR3 Exp.3b Risk             5
## 280  8           0   m  10m_8 10.5        MR3 Exp.3b Risk             5
## 281 12           1   m 10m_12  9.7        MR3 Exp.3b Risk             5
## 282 16           1   m 10m_16  9.6        MR3 Exp.3b Risk             5
## 283 20           0   m 10m_20  9.9        MR3 Exp.3b Risk             5
## 284 24           1   f 10m_24 10.5        MR3 Exp.3b Risk             5
## 285 28           1   f 10m_28 10.4        MR3 Exp.3b Risk             5
## 286 32           1   f 10m_32 10.2        MR3 Exp.3b Risk             5
## 287 36           0   f 10m_36 10.1        MR3 Exp.3b Risk             5
## 288 40           0   m 10m_40 10.8        MR3 Exp.3b Risk             5
## 289  1           1   m  10m_1  9.5        MR3 Exp.3b Risk             5
## 290  5           1   m  10m_5 10.4        MR3 Exp.3b Risk             5
## 291  9           1   m  10m_9  9.8        MR3 Exp.3b Risk             5
## 292 13           0   m 10m_13 10.1        MR3 Exp.3b Risk             5
## 293 17           0   m 10m_17  9.8        MR3 Exp.3b Risk             4
## 294 21           0   f 10m_21 10.7        MR3 Exp.3b Risk             5
## 295 25           0   f 10m_25 10.6        MR3 Exp.3b Risk             5
## 296 29           0   f 10m_29  9.9        MR3 Exp.3b Risk             5
## 297 33           0   f 10m_33  9.8        MR3 Exp.3b Risk             5
## 298 37           1   f 10m_37 10.2        MR3 Exp.3b Risk             5
## 299  2           0   m  10m_2 10.0        MR3 Exp.3b Risk             5
## 300  6           0   m  10m_6 10.7        MR3 Exp.3b Risk             4
## 301 10           0   m 10m_10 10.7        MR3 Exp.3b Risk             5
## 302 14           1   m 10m_14 10.7        MR3 Exp.3b Risk             5
## 303 22           1   f 10m_22 10.3        MR3 Exp.3b Risk             5
## 304 26           1   f 10m_26 11.1        MR3 Exp.3b Risk             5
## 305 30           1   f 10m_30 10.4        MR3 Exp.3b Risk             5
## 306 34           1   f 10m_34  9.8        MR3 Exp.3b Risk             5
## 307 38           1   f 10m_38 10.7        MR3 Exp.3b Risk             4
## 308  3           1   m  10m_3 10.4        MR3 Exp.3b Risk             5
## 309  7           1   m  10m_7 11.0        MR3 Exp.3b Risk             4
## 310 11           0   m 10m_11 10.1        MR3 Exp.3b Risk             5
## 311 15           0   m 10m_15 10.2        MR3 Exp.3b Risk             5
## 312 18           1   f 10m_18 10.2        MR3 Exp.3b Risk             5
## 313 19           1   m 10m_19 10.3        MR3 Exp.3b Risk             5
## 314 23           0   f 10m_23 10.3        MR3 Exp.3b Risk             5
## 315 27           0   f 10m_27 10.2        MR3 Exp.3b Risk             5
## 316 31           1   f 10m_31  9.9        MR3 Exp.3b Risk             4
## 317 35           0   f 10m_35 10.1        MR3 Exp.3b Risk             5
## 318 39           0   f 10m_39 10.0        MR3 Exp.3b Risk             5
## 319  4           0   m  10m_4 10.0        MR3 Exp.3b Risk             5
## 320  8           0   m  10m_8 10.5        MR3 Exp.3b Risk             5
## 321 12           1   m 10m_12  9.7        MR3 Exp.3b Risk             5
## 322 16           1   m 10m_16  9.6        MR3 Exp.3b Risk             5
## 323 20           0   m 10m_20  9.9        MR3 Exp.3b Risk             5
## 324 24           1   f 10m_24 10.5        MR3 Exp.3b Risk             5
## 325 28           1   f 10m_28 10.4        MR3 Exp.3b Risk             5
## 326 32           1   f 10m_32 10.2        MR3 Exp.3b Risk             5
## 327 36           0   f 10m_36 10.1        MR3 Exp.3b Risk             5
## 328 40           0   m 10m_40 10.8        MR3 Exp.3b Risk             5
## 329  1           1   m  10m_1  9.5        MR3 Exp.3b Risk             5
## 330  5           1   m  10m_5 10.4        MR3 Exp.3b Risk             5
## 331  9           1   m  10m_9  9.8        MR3 Exp.3b Risk             5
## 332 13           0   m 10m_13 10.1        MR3 Exp.3b Risk             5
## 333 17           0   m 10m_17  9.8        MR3 Exp.3b Risk             4
## 334 21           0   f 10m_21 10.7        MR3 Exp.3b Risk             5
## 335 25           0   f 10m_25 10.6        MR3 Exp.3b Risk             5
## 336 29           0   f 10m_29  9.9        MR3 Exp.3b Risk             5
## 337 33           0   f 10m_33  9.8        MR3 Exp.3b Risk             5
## 338 37           1   f 10m_37 10.2        MR3 Exp.3b Risk             5
## 339  2           0   m  10m_2 10.0        MR3 Exp.3b Risk             5
## 340  6           0   m  10m_6 10.7        MR3 Exp.3b Risk             4
## 341 10           0   m 10m_10 10.7        MR3 Exp.3b Risk             5
## 342 14           1   m 10m_14 10.7        MR3 Exp.3b Risk             5
## 343 22           1   f 10m_22 10.3        MR3 Exp.3b Risk             5
## 344 26           1   f 10m_26 11.1        MR3 Exp.3b Risk             5
## 345 30           1   f 10m_30 10.4        MR3 Exp.3b Risk             5
## 346 34           1   f 10m_34  9.8        MR3 Exp.3b Risk             5
## 347 38           1   f 10m_38 10.7        MR3 Exp.3b Risk             4
## 348  3           1   m  10m_3 10.4        MR3 Exp.3b Risk             5
## 349  7           1   m  10m_7 11.0        MR3 Exp.3b Risk             4
## 350 11           0   m 10m_11 10.1        MR3 Exp.3b Risk             5
## 351 15           0   m 10m_15 10.2        MR3 Exp.3b Risk             5
## 352 18           1   f 10m_18 10.2        MR3 Exp.3b Risk             5
## 353 19           1   m 10m_19 10.3        MR3 Exp.3b Risk             5
## 354 23           0   f 10m_23 10.3        MR3 Exp.3b Risk             5
## 355 27           0   f 10m_27 10.2        MR3 Exp.3b Risk             5
## 356 31           1   f 10m_31  9.9        MR3 Exp.3b Risk             4
## 357 35           0   f 10m_35 10.1        MR3 Exp.3b Risk             5
## 358 39           0   f 10m_39 10.0        MR3 Exp.3b Risk             5
## 359  4           0   m  10m_4 10.0        MR3 Exp.3b Risk             5
## 360  8           0   m  10m_8 10.5        MR3 Exp.3b Risk             5
## 361 12           1   m 10m_12  9.7        MR3 Exp.3b Risk             5
## 362 16           1   m 10m_16  9.6        MR3 Exp.3b Risk             5
## 363 20           0   m 10m_20  9.9        MR3 Exp.3b Risk             5
## 364 24           1   f 10m_24 10.5        MR3 Exp.3b Risk             5
## 365 28           1   f 10m_28 10.4        MR3 Exp.3b Risk             5
## 366 32           1   f 10m_32 10.2        MR3 Exp.3b Risk             5
## 367 36           0   f 10m_36 10.1        MR3 Exp.3b Risk             5
## 368 40           0   m 10m_40 10.8        MR3 Exp.3b Risk             5
## 369  1           1   m  10m_1  9.5        MR3 Exp.3b Risk             5
## 370  5           1   m  10m_5 10.4        MR3 Exp.3b Risk             5
## 371  9           1   m  10m_9  9.8        MR3 Exp.3b Risk             5
## 372 13           0   m 10m_13 10.1        MR3 Exp.3b Risk             5
## 373 17           0   m 10m_17  9.8        MR3 Exp.3b Risk             4
## 374 21           0   f 10m_21 10.7        MR3 Exp.3b Risk             5
## 375 25           0   f 10m_25 10.6        MR3 Exp.3b Risk             5
## 376 29           0   f 10m_29  9.9        MR3 Exp.3b Risk             5
## 377 33           0   f 10m_33  9.8        MR3 Exp.3b Risk             5
## 378 37           1   f 10m_37 10.2        MR3 Exp.3b Risk             5
## 379  2           0   m  10m_2 10.0        MR3 Exp.3b Risk             5
## 380  6           0   m  10m_6 10.7        MR3 Exp.3b Risk             4
## 381 10           0   m 10m_10 10.7        MR3 Exp.3b Risk             5
## 382 14           1   m 10m_14 10.7        MR3 Exp.3b Risk             5
## 383 22           1   f 10m_22 10.3        MR3 Exp.3b Risk             5
## 384 26           1   f 10m_26 11.1        MR3 Exp.3b Risk             5
## 385 30           1   f 10m_30 10.4        MR3 Exp.3b Risk             5
## 386 34           1   f 10m_34  9.8        MR3 Exp.3b Risk             5
## 387 38           1   f 10m_38 10.7        MR3 Exp.3b Risk             4
## 388  3           1   m  10m_3 10.4        MR3 Exp.3b Risk             5
## 389  7           1   m  10m_7 11.0        MR3 Exp.3b Risk             4
## 390 11           0   m 10m_11 10.1        MR3 Exp.3b Risk             5
## 391 15           0   m 10m_15 10.2        MR3 Exp.3b Risk             5
## 392 18           1   f 10m_18 10.2        MR3 Exp.3b Risk             5
## 393 19           1   m 10m_19 10.3        MR3 Exp.3b Risk             5
## 394 23           0   f 10m_23 10.3        MR3 Exp.3b Risk             5
## 395 27           0   f 10m_27 10.2        MR3 Exp.3b Risk             5
## 396 31           1   f 10m_31  9.9        MR3 Exp.3b Risk             4
## 397 35           0   f 10m_35 10.1        MR3 Exp.3b Risk             5
## 398 39           0   f 10m_39 10.0        MR3 Exp.3b Risk             5
## 399  4           0   m  10m_4 10.0        MR3 Exp.3b Risk             5
## 400  8           0   m  10m_8 10.5        MR3 Exp.3b Risk             5
## 401 12           1   m 10m_12  9.7        MR3 Exp.3b Risk             5
## 402 16           1   m 10m_16  9.6        MR3 Exp.3b Risk             5
## 403 20           0   m 10m_20  9.9        MR3 Exp.3b Risk             5
## 404 24           1   f 10m_24 10.5        MR3 Exp.3b Risk             5
## 405 28           1   f 10m_28 10.4        MR3 Exp.3b Risk             5
## 406 32           1   f 10m_32 10.2        MR3 Exp.3b Risk             5
## 407 36           0   f 10m_36 10.1        MR3 Exp.3b Risk             5
## 408 40           0   m 10m_40 10.8        MR3 Exp.3b Risk             5
##     audio_quality device highchair HV_side first_test first_fam
## 1              NA               NA   right         LV        LH
## 2              NA               NA    left         LV        HL
## 3              NA               NA    left         HV        LH
## 4              NA               NA   right         LV        HL
## 5              NA               NA   right         HV        LH
## 6              NA               NA    left         LV        HL
## 7              NA               NA   right         LV        LH
## 8              NA               NA   right         LV        LH
## 9              NA               NA   right         HV        HL
## 10             NA               NA    left         LV        LH
## 11             NA               NA    left         LV        LH
## 12             NA               NA   right         LV        HL
## 13             NA               NA   right         HV        LH
## 14             NA               NA    left         HV        LH
## 15             NA               NA    left         LV        HL
## 16             NA               NA   right         LV        LH
## 17             NA               NA   right         HV        HL
## 18             NA               NA    left         HV        HL
## 19             NA               NA    left         HV        HL
## 20             NA               NA   right         HV        HL
## 21             NA               NA   right         LV        HL
## 22             NA               NA   right         HV        LH
## 23             NA               NA    left         LV        LH
## 24             NA               NA    left         HV        LH
## 25             NA               NA   right         HV        LH
## 26             NA               NA    left         HV        HL
## 27             NA               NA    left         LV        HL
## 28             NA               NA    left         HV        LH
## 29             NA               NA    left         HV        HL
## 30             NA               NA   right         LV        HL
## 31             NA               NA    left         LV        LH
## 32             NA               NA   right         HV        HL
## 33             NA               NA   right         LV        LH
## 34             NA               NA    left         LV        HL
## 35             NA               NA    left         HV        LH
## 36             NA               NA   right         LV        HL
## 37             NA               NA   right         HV        LH
## 38             NA               NA    left         LV        HL
## 39             NA               NA   right         LV        LH
## 40             NA               NA   right         LV        LH
## 41             NA               NA   right         HV        HL
## 42             NA               NA    left         LV        LH
## 43             NA               NA    left         LV        LH
## 44             NA               NA   right         LV        HL
## 45             NA               NA   right         HV        LH
## 46             NA               NA    left         HV        LH
## 47             NA               NA    left         LV        HL
## 48             NA               NA   right         LV        LH
## 49             NA               NA   right         HV        HL
## 50             NA               NA    left         HV        HL
## 51             NA               NA    left         HV        HL
## 52             NA               NA   right         HV        HL
## 53             NA               NA   right         LV        HL
## 54             NA               NA   right         HV        LH
## 55             NA               NA    left         LV        LH
## 56             NA               NA    left         HV        LH
## 57             NA               NA   right         HV        LH
## 58             NA               NA    left         HV        HL
## 59             NA               NA    left         LV        HL
## 60             NA               NA    left         HV        LH
## 61             NA               NA    left         HV        HL
## 62             NA               NA   right         LV        HL
## 63             NA               NA    left         LV        LH
## 64             NA               NA   right         HV        HL
## 65             NA               NA   right         LV        LH
## 66             NA               NA    left         LV        HL
## 67             NA               NA    left         HV        LH
## 68             NA               NA   right         LV        HL
## 69             NA               NA   right         HV        LH
## 70             NA               NA    left         LV        HL
## 71             NA               NA   right         LV        LH
## 72             NA               NA   right         LV        LH
## 73             NA               NA   right         HV        HL
## 74             NA               NA    left         LV        LH
## 75             NA               NA    left         LV        LH
## 76             NA               NA   right         LV        HL
## 77             NA               NA   right         HV        LH
## 78             NA               NA    left         HV        LH
## 79             NA               NA    left         LV        HL
## 80             NA               NA   right         LV        LH
## 81             NA               NA   right         HV        HL
## 82             NA               NA    left         HV        HL
## 83             NA               NA    left         HV        HL
## 84             NA               NA   right         HV        HL
## 85             NA               NA   right         LV        HL
## 86             NA               NA   right         HV        LH
## 87             NA               NA    left         LV        LH
## 88             NA               NA    left         HV        LH
## 89             NA               NA   right         HV        LH
## 90             NA               NA    left         HV        HL
## 91             NA               NA    left         LV        HL
## 92             NA               NA    left         HV        LH
## 93             NA               NA    left         HV        HL
## 94             NA               NA   right         LV        HL
## 95             NA               NA    left         LV        LH
## 96             NA               NA   right         HV        HL
## 97             NA               NA   right         LV        LH
## 98             NA               NA    left         LV        HL
## 99             NA               NA    left         HV        LH
## 100            NA               NA   right         LV        HL
## 101            NA               NA   right         HV        LH
## 102            NA               NA    left         LV        HL
## 103            NA               NA   right         LV        LH
## 104            NA               NA   right         LV        LH
## 105            NA               NA   right         HV        HL
## 106            NA               NA    left         LV        LH
## 107            NA               NA    left         LV        LH
## 108            NA               NA   right         LV        HL
## 109            NA               NA   right         HV        LH
## 110            NA               NA    left         HV        LH
## 111            NA               NA    left         LV        HL
## 112            NA               NA   right         LV        LH
## 113            NA               NA   right         HV        HL
## 114            NA               NA    left         HV        HL
## 115            NA               NA    left         HV        HL
## 116            NA               NA   right         HV        HL
## 117            NA               NA   right         LV        HL
## 118            NA               NA   right         HV        LH
## 119            NA               NA    left         LV        LH
## 120            NA               NA    left         HV        LH
## 121            NA               NA   right         HV        LH
## 122            NA               NA    left         HV        HL
## 123            NA               NA    left         LV        HL
## 124            NA               NA    left         HV        LH
## 125            NA               NA    left         HV        HL
## 126            NA               NA   right         LV        HL
## 127            NA               NA    left         LV        LH
## 128            NA               NA   right         HV        HL
## 129            NA               NA            shallow      fam1
## 130            NA               NA            shallow      fam2
## 131            NA               NA               deep      fam1
## 132            NA               NA            shallow      fam2
## 133            NA               NA               deep      fam2
## 134            NA               NA            shallow      fam1
## 135            NA               NA            shallow      fam1
## 136            NA               NA            shallow      fam1
## 137            NA               NA               deep      fam1
## 138            NA               NA            shallow      fam1
## 139            NA               NA               deep      fam1
## 140            NA               NA            shallow      fam2
## 141            NA               NA               deep      fam1
## 142            NA               NA               deep      fam2
## 143            NA               NA            shallow      fam2
## 144            NA               NA               deep      fam1
## 145            NA               NA            shallow      fam2
## 146            NA               NA               deep      fam2
## 147            NA               NA               deep      fam2
## 148            NA               NA               deep      fam2
## 149            NA               NA            shallow      fam2
## 150            NA               NA               deep      fam1
## 151            NA               NA            shallow      fam1
## 152            NA               NA            shallow      fam1
## 153            NA               NA            shallow      fam1
## 154            NA               NA               deep      fam1
## 155            NA               NA               deep      fam2
## 156            NA               NA            shallow      fam2
## 157            NA               NA            shallow      fam2
## 158            NA               NA               deep      fam2
## 159            NA               NA            shallow      fam1
## 160            NA               NA            shallow      fam2
## 161            NA               NA               deep      fam1
## 162            NA               NA            shallow      fam2
## 163            NA               NA               deep      fam2
## 164            NA               NA            shallow      fam1
## 165            NA               NA            shallow      fam1
## 166            NA               NA            shallow      fam1
## 167            NA               NA               deep      fam1
## 168            NA               NA            shallow      fam1
## 169            NA               NA               deep      fam1
## 170            NA               NA            shallow      fam2
## 171            NA               NA               deep      fam1
## 172            NA               NA               deep      fam2
## 173            NA               NA            shallow      fam2
## 174            NA               NA               deep      fam1
## 175            NA               NA            shallow      fam2
## 176            NA               NA               deep      fam2
## 177            NA               NA               deep      fam2
## 178            NA               NA               deep      fam2
## 179            NA               NA            shallow      fam2
## 180            NA               NA               deep      fam1
## 181            NA               NA            shallow      fam1
## 182            NA               NA            shallow      fam1
## 183            NA               NA            shallow      fam1
## 184            NA               NA               deep      fam1
## 185            NA               NA               deep      fam2
## 186            NA               NA            shallow      fam2
## 187            NA               NA            shallow      fam2
## 188            NA               NA               deep      fam2
## 189            NA               NA            shallow      fam1
## 190            NA               NA            shallow      fam2
## 191            NA               NA               deep      fam1
## 192            NA               NA            shallow      fam2
## 193            NA               NA               deep      fam2
## 194            NA               NA            shallow      fam1
## 195            NA               NA            shallow      fam1
## 196            NA               NA            shallow      fam1
## 197            NA               NA               deep      fam1
## 198            NA               NA            shallow      fam1
## 199            NA               NA               deep      fam1
## 200            NA               NA            shallow      fam2
## 201            NA               NA               deep      fam1
## 202            NA               NA               deep      fam2
## 203            NA               NA            shallow      fam2
## 204            NA               NA               deep      fam1
## 205            NA               NA            shallow      fam2
## 206            NA               NA               deep      fam2
## 207            NA               NA               deep      fam2
## 208            NA               NA               deep      fam2
## 209            NA               NA            shallow      fam2
## 210            NA               NA               deep      fam1
## 211            NA               NA            shallow      fam1
## 212            NA               NA            shallow      fam1
## 213            NA               NA            shallow      fam1
## 214            NA               NA               deep      fam1
## 215            NA               NA               deep      fam2
## 216            NA               NA            shallow      fam2
## 217            NA               NA            shallow      fam2
## 218            NA               NA               deep      fam2
## 219            NA               NA            shallow      fam1
## 220            NA               NA            shallow      fam2
## 221            NA               NA               deep      fam1
## 222            NA               NA            shallow      fam2
## 223            NA               NA               deep      fam2
## 224            NA               NA            shallow      fam1
## 225            NA               NA            shallow      fam1
## 226            NA               NA            shallow      fam1
## 227            NA               NA               deep      fam1
## 228            NA               NA            shallow      fam1
## 229            NA               NA               deep      fam1
## 230            NA               NA            shallow      fam2
## 231            NA               NA               deep      fam1
## 232            NA               NA               deep      fam2
## 233            NA               NA            shallow      fam2
## 234            NA               NA               deep      fam1
## 235            NA               NA            shallow      fam2
## 236            NA               NA               deep      fam2
## 237            NA               NA               deep      fam2
## 238            NA               NA               deep      fam2
## 239            NA               NA            shallow      fam2
## 240            NA               NA               deep      fam1
## 241            NA               NA            shallow      fam1
## 242            NA               NA            shallow      fam1
## 243            NA               NA            shallow      fam1
## 244            NA               NA               deep      fam1
## 245            NA               NA               deep      fam2
## 246            NA               NA            shallow      fam2
## 247            NA               NA            shallow      fam2
## 248            NA               NA               deep      fam2
## 249           5.0   ipad         0       A       deep      fam1
## 250           5.0 laptop         0       A       deep      fam1
## 251           5.0 laptop         0       A       deep      fam1
## 252           5.0   ipad         1       A       deep      fam1
## 253           5.0   ipad         1       A       deep      fam1
## 254           5.0 laptop         0       A       deep      fam1
## 255           5.0 laptop         1       A       deep      fam1
## 256           5.0   ipad         0       A       deep      fam1
## 257           5.0 laptop         0       A       deep      fam1
## 258           5.0 laptop         1       A       deep      fam1
## 259           5.0 laptop         0       B    shallow      fam2
## 260           4.0   ipad         1       B    shallow      fam2
## 261           5.0 laptop         0       B    shallow      fam2
## 262           5.0   ipad         1       B    shallow      fam2
## 263           5.0   ipad         0       B    shallow      fam2
## 264           5.0 laptop         0       B    shallow      fam2
## 265           5.0 laptop         1       B    shallow      fam2
## 266           5.0 laptop         0       B    shallow      fam2
## 267           5.0   ipad         1       B    shallow      fam2
## 268           5.0 laptop         1       C    shallow      fam1
## 269           4.0   ipad         1       C    shallow      fam1
## 270           5.0 laptop         0       C    shallow      fam1
## 271           5.0 laptop         0       C    shallow      fam1
## 272           5.0 laptop         0       C    shallow      fam1
## 273           5.0 laptop         0       C    shallow      fam1
## 274           5.0 laptop         0       C    shallow      fam1
## 275           5.0 laptop         0       C    shallow      fam1
## 276           4.0 laptop         0       C    shallow      fam1
## 277           5.0 laptop         0       C    shallow      fam1
## 278           5.0 laptop         0       C    shallow      fam1
## 279           5.0   ipad         0       D       deep      fam2
## 280           5.0 laptop         1       D       deep      fam2
## 281           4.0 laptop         0       D       deep      fam2
## 282           5.0 laptop         1       D       deep      fam2
## 283           5.0 laptop         1       D       deep      fam2
## 284           5.0   ipad         0       D       deep      fam2
## 285           4.0 laptop         0       D       deep      fam2
## 286           5.0 laptop         0       D       deep      fam2
## 287           5.0   ipad         0       D       deep      fam2
## 288           4.5 laptop         0       D       deep      fam2
## 289           5.0   ipad         0       A       deep      fam1
## 290           5.0 laptop         0       A       deep      fam1
## 291           5.0 laptop         0       A       deep      fam1
## 292           5.0   ipad         1       A       deep      fam1
## 293           5.0   ipad         1       A       deep      fam1
## 294           5.0 laptop         0       A       deep      fam1
## 295           5.0 laptop         1       A       deep      fam1
## 296           5.0   ipad         0       A       deep      fam1
## 297           5.0 laptop         0       A       deep      fam1
## 298           5.0 laptop         1       A       deep      fam1
## 299           5.0 laptop         0       B    shallow      fam2
## 300           4.0   ipad         1       B    shallow      fam2
## 301           5.0 laptop         0       B    shallow      fam2
## 302           5.0   ipad         1       B    shallow      fam2
## 303           5.0   ipad         0       B    shallow      fam2
## 304           5.0 laptop         0       B    shallow      fam2
## 305           5.0 laptop         1       B    shallow      fam2
## 306           5.0 laptop         0       B    shallow      fam2
## 307           5.0   ipad         1       B    shallow      fam2
## 308           5.0 laptop         1       C    shallow      fam1
## 309           4.0   ipad         1       C    shallow      fam1
## 310           5.0 laptop         0       C    shallow      fam1
## 311           5.0 laptop         0       C    shallow      fam1
## 312           5.0 laptop         0       C    shallow      fam1
## 313           5.0 laptop         0       C    shallow      fam1
## 314           5.0 laptop         0       C    shallow      fam1
## 315           5.0 laptop         0       C    shallow      fam1
## 316           4.0 laptop         0       C    shallow      fam1
## 317           5.0 laptop         0       C    shallow      fam1
## 318           5.0 laptop         0       C    shallow      fam1
## 319           5.0   ipad         0       D       deep      fam2
## 320           5.0 laptop         1       D       deep      fam2
## 321           4.0 laptop         0       D       deep      fam2
## 322           5.0 laptop         1       D       deep      fam2
## 323           5.0 laptop         1       D       deep      fam2
## 324           5.0   ipad         0       D       deep      fam2
## 325           4.0 laptop         0       D       deep      fam2
## 326           5.0 laptop         0       D       deep      fam2
## 327           5.0   ipad         0       D       deep      fam2
## 328           4.5 laptop         0       D       deep      fam2
## 329           5.0   ipad         0       A       deep      fam1
## 330           5.0 laptop         0       A       deep      fam1
## 331           5.0 laptop         0       A       deep      fam1
## 332           5.0   ipad         1       A       deep      fam1
## 333           5.0   ipad         1       A       deep      fam1
## 334           5.0 laptop         0       A       deep      fam1
## 335           5.0 laptop         1       A       deep      fam1
## 336           5.0   ipad         0       A       deep      fam1
## 337           5.0 laptop         0       A       deep      fam1
## 338           5.0 laptop         1       A       deep      fam1
## 339           5.0 laptop         0       B    shallow      fam2
## 340           4.0   ipad         1       B    shallow      fam2
## 341           5.0 laptop         0       B    shallow      fam2
## 342           5.0   ipad         1       B    shallow      fam2
## 343           5.0   ipad         0       B    shallow      fam2
## 344           5.0 laptop         0       B    shallow      fam2
## 345           5.0 laptop         1       B    shallow      fam2
## 346           5.0 laptop         0       B    shallow      fam2
## 347           5.0   ipad         1       B    shallow      fam2
## 348           5.0 laptop         1       C    shallow      fam1
## 349           4.0   ipad         1       C    shallow      fam1
## 350           5.0 laptop         0       C    shallow      fam1
## 351           5.0 laptop         0       C    shallow      fam1
## 352           5.0 laptop         0       C    shallow      fam1
## 353           5.0 laptop         0       C    shallow      fam1
## 354           5.0 laptop         0       C    shallow      fam1
## 355           5.0 laptop         0       C    shallow      fam1
## 356           4.0 laptop         0       C    shallow      fam1
## 357           5.0 laptop         0       C    shallow      fam1
## 358           5.0 laptop         0       C    shallow      fam1
## 359           5.0   ipad         0       D       deep      fam2
## 360           5.0 laptop         1       D       deep      fam2
## 361           4.0 laptop         0       D       deep      fam2
## 362           5.0 laptop         1       D       deep      fam2
## 363           5.0 laptop         1       D       deep      fam2
## 364           5.0   ipad         0       D       deep      fam2
## 365           4.0 laptop         0       D       deep      fam2
## 366           5.0 laptop         0       D       deep      fam2
## 367           5.0   ipad         0       D       deep      fam2
## 368           4.5 laptop         0       D       deep      fam2
## 369           5.0   ipad         0       A       deep      fam1
## 370           5.0 laptop         0       A       deep      fam1
## 371           5.0 laptop         0       A       deep      fam1
## 372           5.0   ipad         1       A       deep      fam1
## 373           5.0   ipad         1       A       deep      fam1
## 374           5.0 laptop         0       A       deep      fam1
## 375           5.0 laptop         1       A       deep      fam1
## 376           5.0   ipad         0       A       deep      fam1
## 377           5.0 laptop         0       A       deep      fam1
## 378           5.0 laptop         1       A       deep      fam1
## 379           5.0 laptop         0       B    shallow      fam2
## 380           4.0   ipad         1       B    shallow      fam2
## 381           5.0 laptop         0       B    shallow      fam2
## 382           5.0   ipad         1       B    shallow      fam2
## 383           5.0   ipad         0       B    shallow      fam2
## 384           5.0 laptop         0       B    shallow      fam2
## 385           5.0 laptop         1       B    shallow      fam2
## 386           5.0 laptop         0       B    shallow      fam2
## 387           5.0   ipad         1       B    shallow      fam2
## 388           5.0 laptop         1       C    shallow      fam1
## 389           4.0   ipad         1       C    shallow      fam1
## 390           5.0 laptop         0       C    shallow      fam1
## 391           5.0 laptop         0       C    shallow      fam1
## 392           5.0 laptop         0       C    shallow      fam1
## 393           5.0 laptop         0       C    shallow      fam1
## 394           5.0 laptop         0       C    shallow      fam1
## 395           5.0 laptop         0       C    shallow      fam1
## 396           4.0 laptop         0       C    shallow      fam1
## 397           5.0 laptop         0       C    shallow      fam1
## 398           5.0 laptop         0       C    shallow      fam1
## 399           5.0   ipad         0       D       deep      fam2
## 400           5.0 laptop         1       D       deep      fam2
## 401           4.0 laptop         0       D       deep      fam2
## 402           5.0 laptop         1       D       deep      fam2
## 403           5.0 laptop         1       D       deep      fam2
## 404           5.0   ipad         0       D       deep      fam2
## 405           4.0 laptop         0       D       deep      fam2
## 406           5.0 laptop         0       D       deep      fam2
## 407           5.0   ipad         0       D       deep      fam2
## 408           4.5 laptop         0       D       deep      fam2
##     first_test_deeper_side control_deeper_side control_firstevent control_1
## 1                                                                          
## 2                                                                          
## 3                                                                          
## 4                                                                          
## 5                                                                          
## 6                                                                          
## 7                                                                          
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## 95                                                                         
## 96                                                                         
## 97                                                                         
## 98                                                                         
## 99                                                                         
## 100                                                                        
## 101                                                                        
## 102                                                                        
## 103                                                                        
## 104                                                                        
## 105                                                                        
## 106                                                                        
## 107                                                                        
## 108                                                                        
## 109                                                                        
## 110                                                                        
## 111                                                                        
## 112                                                                        
## 113                                                                        
## 114                                                                        
## 115                                                                        
## 116                                                                        
## 117                                                                        
## 118                                                                        
## 119                                                                        
## 120                                                                        
## 121                                                                        
## 122                                                                        
## 123                                                                        
## 124                                                                        
## 125                                                                        
## 126                                                                        
## 127                                                                        
## 128                                                                        
## 129                  right                left               deep     26.15
## 130                   left                left               deep      4.59
## 131                   left               right            shallow     14.55
## 132                   left                left               deep      5.37
## 133                  right               right            shallow     14.99
## 134                  right                left               deep      10.2
## 135                  right                left               deep      9.76
## 136                  right                left               deep     16.97
## 137                   left               right            shallow        22
## 138                  right                left               deep        10
## 139                   left               right            shallow      9.62
## 140                   left                left               deep     13.23
## 141                   left               right            shallow     19.28
## 142                  right               right            shallow     13.02
## 143                   left                left               deep     10.47
## 144                   left               right            shallow     11.29
## 145                   left                left               deep      5.85
## 146                  right               right            shallow      9.35
## 147                  right               right            shallow     19.89
## 148                  right               right            shallow     26.01
## 149                   left                left               deep     17.65
## 150                   left               right            shallow      11.9
## 151                  right                left               deep     16.32
## 152                  right                left               deep      5.88
## 153                  right                left               deep     19.21
## 154                   left               right            shallow     10.95
## 155                  right               right            shallow     13.53
## 156                   left                left               deep     16.12
## 157                   left                left               deep     22.88
## 158                  right               right            shallow      4.56
## 159                  right                left               deep     26.15
## 160                   left                left               deep      4.59
## 161                   left               right            shallow     14.55
## 162                   left                left               deep      5.37
## 163                  right               right            shallow     14.99
## 164                  right                left               deep      10.2
## 165                  right                left               deep      9.76
## 166                  right                left               deep     16.97
## 167                   left               right            shallow        22
## 168                  right                left               deep        10
## 169                   left               right            shallow      9.62
## 170                   left                left               deep     13.23
## 171                   left               right            shallow     19.28
## 172                  right               right            shallow     13.02
## 173                   left                left               deep     10.47
## 174                   left               right            shallow     11.29
## 175                   left                left               deep      5.85
## 176                  right               right            shallow      9.35
## 177                  right               right            shallow     19.89
## 178                  right               right            shallow     26.01
## 179                   left                left               deep     17.65
## 180                   left               right            shallow      11.9
## 181                  right                left               deep     16.32
## 182                  right                left               deep      5.88
## 183                  right                left               deep     19.21
## 184                   left               right            shallow     10.95
## 185                  right               right            shallow     13.53
## 186                   left                left               deep     16.12
## 187                   left                left               deep     22.88
## 188                  right               right            shallow      4.56
## 189                  right                left               deep     26.15
## 190                   left                left               deep      4.59
## 191                   left               right            shallow     14.55
## 192                   left                left               deep      5.37
## 193                  right               right            shallow     14.99
## 194                  right                left               deep      10.2
## 195                  right                left               deep      9.76
## 196                  right                left               deep     16.97
## 197                   left               right            shallow        22
## 198                  right                left               deep        10
## 199                   left               right            shallow      9.62
## 200                   left                left               deep     13.23
## 201                   left               right            shallow     19.28
## 202                  right               right            shallow     13.02
## 203                   left                left               deep     10.47
## 204                   left               right            shallow     11.29
## 205                   left                left               deep      5.85
## 206                  right               right            shallow      9.35
## 207                  right               right            shallow     19.89
## 208                  right               right            shallow     26.01
## 209                   left                left               deep     17.65
## 210                   left               right            shallow      11.9
## 211                  right                left               deep     16.32
## 212                  right                left               deep      5.88
## 213                  right                left               deep     19.21
## 214                   left               right            shallow     10.95
## 215                  right               right            shallow     13.53
## 216                   left                left               deep     16.12
## 217                   left                left               deep     22.88
## 218                  right               right            shallow      4.56
## 219                  right                left               deep     26.15
## 220                   left                left               deep      4.59
## 221                   left               right            shallow     14.55
## 222                   left                left               deep      5.37
## 223                  right               right            shallow     14.99
## 224                  right                left               deep      10.2
## 225                  right                left               deep      9.76
## 226                  right                left               deep     16.97
## 227                   left               right            shallow        22
## 228                  right                left               deep        10
## 229                   left               right            shallow      9.62
## 230                   left                left               deep     13.23
## 231                   left               right            shallow     19.28
## 232                  right               right            shallow     13.02
## 233                   left                left               deep     10.47
## 234                   left               right            shallow     11.29
## 235                   left                left               deep      5.85
## 236                  right               right            shallow      9.35
## 237                  right               right            shallow     19.89
## 238                  right               right            shallow     26.01
## 239                   left                left               deep     17.65
## 240                   left               right            shallow      11.9
## 241                  right                left               deep     16.32
## 242                  right                left               deep      5.88
## 243                  right                left               deep     19.21
## 244                   left               right            shallow     10.95
## 245                  right               right            shallow     13.53
## 246                   left                left               deep     16.12
## 247                   left                left               deep     22.88
## 248                  right               right            shallow      4.56
## 249                  right               right               deep     9.548
## 250                  right               right               deep      4.96
## 251                  right               right               deep     13.02
## 252                  right               right               deep    20.088
## 253                  right               right               deep    11.656
## 254                  right               right               deep    31.372
## 255                  right               right               deep      8.37
## 256                  right               right               deep    14.074
## 257                  right               right               deep      4.03
## 258                  right               right               deep    20.328
## 259                  right               right            shallow    21.824
## 260                  right               right            shallow     26.35
## 261                  right               right            shallow     12.09
## 262                  right               right            shallow      7.13
## 263                  right               right            shallow    16.058
## 264                  right               right            shallow    12.772
## 265                  right               right            shallow NA_13.578
## 266                  right               right            shallow    23.002
## 267                  right               right            shallow    10.292
## 268                   left                left            shallow     9.796
## 269                   left                left            shallow     6.262
## 270                   left                left            shallow     6.572
## 271                   left                left            shallow    11.284
## 272                   left                left            shallow    23.064
## 273                   left                left            shallow    11.583
## 274                   left                left            shallow    21.948
## 275                   left                left            shallow     17.98
## 276                   left                left            shallow      9.92
## 277                   left                left            shallow      12.4
## 278                   left                left            shallow    19.107
## 279                   left                left               deep    24.366
## 280                   left                left               deep    14.446
## 281                   left                left               deep     9.672
## 282                   left                left               deep    17.112
## 283                   left                left               deep     6.882
## 284                   left                left               deep  NA_27.28
## 285                   left                left               deep NA_10.044
## 286                   left                left               deep    11.844
## 287                   left                left               deep    10.912
## 288                   left                left               deep    15.004
## 289                  right               right               deep     9.548
## 290                  right               right               deep      4.96
## 291                  right               right               deep     13.02
## 292                  right               right               deep    20.088
## 293                  right               right               deep    11.656
## 294                  right               right               deep    31.372
## 295                  right               right               deep      8.37
## 296                  right               right               deep    14.074
## 297                  right               right               deep      4.03
## 298                  right               right               deep    20.328
## 299                  right               right            shallow    21.824
## 300                  right               right            shallow     26.35
## 301                  right               right            shallow     12.09
## 302                  right               right            shallow      7.13
## 303                  right               right            shallow    16.058
## 304                  right               right            shallow    12.772
## 305                  right               right            shallow NA_13.578
## 306                  right               right            shallow    23.002
## 307                  right               right            shallow    10.292
## 308                   left                left            shallow     9.796
## 309                   left                left            shallow     6.262
## 310                   left                left            shallow     6.572
## 311                   left                left            shallow    11.284
## 312                   left                left            shallow    23.064
## 313                   left                left            shallow    11.583
## 314                   left                left            shallow    21.948
## 315                   left                left            shallow     17.98
## 316                   left                left            shallow      9.92
## 317                   left                left            shallow      12.4
## 318                   left                left            shallow    19.107
## 319                   left                left               deep    24.366
## 320                   left                left               deep    14.446
## 321                   left                left               deep     9.672
## 322                   left                left               deep    17.112
## 323                   left                left               deep     6.882
## 324                   left                left               deep  NA_27.28
## 325                   left                left               deep NA_10.044
## 326                   left                left               deep    11.844
## 327                   left                left               deep    10.912
## 328                   left                left               deep    15.004
## 329                  right               right               deep     9.548
## 330                  right               right               deep      4.96
## 331                  right               right               deep     13.02
## 332                  right               right               deep    20.088
## 333                  right               right               deep    11.656
## 334                  right               right               deep    31.372
## 335                  right               right               deep      8.37
## 336                  right               right               deep    14.074
## 337                  right               right               deep      4.03
## 338                  right               right               deep    20.328
## 339                  right               right            shallow    21.824
## 340                  right               right            shallow     26.35
## 341                  right               right            shallow     12.09
## 342                  right               right            shallow      7.13
## 343                  right               right            shallow    16.058
## 344                  right               right            shallow    12.772
## 345                  right               right            shallow NA_13.578
## 346                  right               right            shallow    23.002
## 347                  right               right            shallow    10.292
## 348                   left                left            shallow     9.796
## 349                   left                left            shallow     6.262
## 350                   left                left            shallow     6.572
## 351                   left                left            shallow    11.284
## 352                   left                left            shallow    23.064
## 353                   left                left            shallow    11.583
## 354                   left                left            shallow    21.948
## 355                   left                left            shallow     17.98
## 356                   left                left            shallow      9.92
## 357                   left                left            shallow      12.4
## 358                   left                left            shallow    19.107
## 359                   left                left               deep    24.366
## 360                   left                left               deep    14.446
## 361                   left                left               deep     9.672
## 362                   left                left               deep    17.112
## 363                   left                left               deep     6.882
## 364                   left                left               deep  NA_27.28
## 365                   left                left               deep NA_10.044
## 366                   left                left               deep    11.844
## 367                   left                left               deep    10.912
## 368                   left                left               deep    15.004
## 369                  right               right               deep     9.548
## 370                  right               right               deep      4.96
## 371                  right               right               deep     13.02
## 372                  right               right               deep    20.088
## 373                  right               right               deep    11.656
## 374                  right               right               deep    31.372
## 375                  right               right               deep      8.37
## 376                  right               right               deep    14.074
## 377                  right               right               deep      4.03
## 378                  right               right               deep    20.328
## 379                  right               right            shallow    21.824
## 380                  right               right            shallow     26.35
## 381                  right               right            shallow     12.09
## 382                  right               right            shallow      7.13
## 383                  right               right            shallow    16.058
## 384                  right               right            shallow    12.772
## 385                  right               right            shallow NA_13.578
## 386                  right               right            shallow    23.002
## 387                  right               right            shallow    10.292
## 388                   left                left            shallow     9.796
## 389                   left                left            shallow     6.262
## 390                   left                left            shallow     6.572
## 391                   left                left            shallow    11.284
## 392                   left                left            shallow    23.064
## 393                   left                left            shallow    11.583
## 394                   left                left            shallow    21.948
## 395                   left                left            shallow     17.98
## 396                   left                left            shallow      9.92
## 397                   left                left            shallow      12.4
## 398                   left                left            shallow    19.107
## 399                   left                left               deep    24.366
## 400                   left                left               deep    14.446
## 401                   left                left               deep     9.672
## 402                   left                left               deep    17.112
## 403                   left                left               deep     6.882
## 404                   left                left               deep  NA_27.28
## 405                   left                left               deep NA_10.044
## 406                   left                left               deep    11.844
## 407                   left                left               deep    10.912
## 408                   left                left               deep    15.004
##     control_2      fam1      fam2     fam3      fam4     fam5      fam6
## 1                    60        60     21.3       3.6      5.5       2.7
## 2                    60      53.2     33.2       5.1      8.5       4.8
## 3                    60        60     36.5      40.7     30.6      13.4
## 4                    60        60     44.9      11.3     23.9      16.6
## 5                    60       5.6       60      48.7      2.9       3.9
## 6                    60      39.9     15.5       4.8     10.8         7
## 7                    60      42.5       60      42.1     21.7      22.7
## 8                  34.9      15.2      4.6       3.1      4.3       6.6
## 9                    60      12.3     10.8      32.5     58.7      13.9
## 10                   60        60       60        60     14.6      27.8
## 11                   60        60     45.8      19.9     20.4      25.7
## 12                 23.9      27.2       57      12.9      4.8        60
## 13                   60      24.7     31.4      21.7     19.5      25.6
## 14                 33.6        60     14.8      23.3     14.3       6.4
## 15                 31.2       6.3      6.3       5.6      3.7      12.8
## 16                 23.4       8.3     12.2       8.5      3.1         3
## 17                   60        60     53.2      58.5      6.9       3.5
## 18                   60        60        6      14.3     27.2       4.7
## 19                   60        60       60      34.3     21.8      10.3
## 20                   60        60     29.2        60     48.4         4
## 21                   60        60     35.2      17.9     10.8      21.1
## 22                   60        60       60      27.5     12.4       9.9
## 23                   60      25.3       49      12.1     17.7      10.5
## 24                 47.1        60     25.2      12.3      3.5       8.9
## 25                   60        60       60      11.3     21.1      10.4
## 26                   60        60     39.6      32.1      5.1       4.7
## 27                   60      52.8       60      50.1     21.4      14.2
## 28                 <NA>      <NA>     <NA>      <NA>     <NA>      <NA>
## 29                28.99     20.57    46.76     37.78    40.04      6.85
## 30                 59.6      19.4     10.8       4.2      3.7       3.9
## 31                   60        60     23.2      42.9       14      22.6
## 32                   60        60       60        60       60        60
## 33                   60        60     21.3       3.6      5.5       2.7
## 34                   60      53.2     33.2       5.1      8.5       4.8
## 35                   60        60     36.5      40.7     30.6      13.4
## 36                   60        60     44.9      11.3     23.9      16.6
## 37                   60       5.6       60      48.7      2.9       3.9
## 38                   60      39.9     15.5       4.8     10.8         7
## 39                   60      42.5       60      42.1     21.7      22.7
## 40                 34.9      15.2      4.6       3.1      4.3       6.6
## 41                   60      12.3     10.8      32.5     58.7      13.9
## 42                   60        60       60        60     14.6      27.8
## 43                   60        60     45.8      19.9     20.4      25.7
## 44                 23.9      27.2       57      12.9      4.8        60
## 45                   60      24.7     31.4      21.7     19.5      25.6
## 46                 33.6        60     14.8      23.3     14.3       6.4
## 47                 31.2       6.3      6.3       5.6      3.7      12.8
## 48                 23.4       8.3     12.2       8.5      3.1         3
## 49                   60        60     53.2      58.5      6.9       3.5
## 50                   60        60        6      14.3     27.2       4.7
## 51                   60        60       60      34.3     21.8      10.3
## 52                   60        60     29.2        60     48.4         4
## 53                   60        60     35.2      17.9     10.8      21.1
## 54                   60        60       60      27.5     12.4       9.9
## 55                   60      25.3       49      12.1     17.7      10.5
## 56                 47.1        60     25.2      12.3      3.5       8.9
## 57                   60        60       60      11.3     21.1      10.4
## 58                   60        60     39.6      32.1      5.1       4.7
## 59                   60      52.8       60      50.1     21.4      14.2
## 60                 <NA>      <NA>     <NA>      <NA>     <NA>      <NA>
## 61                28.99     20.57    46.76     37.78    40.04      6.85
## 62                 59.6      19.4     10.8       4.2      3.7       3.9
## 63                   60        60     23.2      42.9       14      22.6
## 64                   60        60       60        60       60        60
## 65                   60        60     21.3       3.6      5.5       2.7
## 66                   60      53.2     33.2       5.1      8.5       4.8
## 67                   60        60     36.5      40.7     30.6      13.4
## 68                   60        60     44.9      11.3     23.9      16.6
## 69                   60       5.6       60      48.7      2.9       3.9
## 70                   60      39.9     15.5       4.8     10.8         7
## 71                   60      42.5       60      42.1     21.7      22.7
## 72                 34.9      15.2      4.6       3.1      4.3       6.6
## 73                   60      12.3     10.8      32.5     58.7      13.9
## 74                   60        60       60        60     14.6      27.8
## 75                   60        60     45.8      19.9     20.4      25.7
## 76                 23.9      27.2       57      12.9      4.8        60
## 77                   60      24.7     31.4      21.7     19.5      25.6
## 78                 33.6        60     14.8      23.3     14.3       6.4
## 79                 31.2       6.3      6.3       5.6      3.7      12.8
## 80                 23.4       8.3     12.2       8.5      3.1         3
## 81                   60        60     53.2      58.5      6.9       3.5
## 82                   60        60        6      14.3     27.2       4.7
## 83                   60        60       60      34.3     21.8      10.3
## 84                   60        60     29.2        60     48.4         4
## 85                   60        60     35.2      17.9     10.8      21.1
## 86                   60        60       60      27.5     12.4       9.9
## 87                   60      25.3       49      12.1     17.7      10.5
## 88                 47.1        60     25.2      12.3      3.5       8.9
## 89                   60        60       60      11.3     21.1      10.4
## 90                   60        60     39.6      32.1      5.1       4.7
## 91                   60      52.8       60      50.1     21.4      14.2
## 92                 <NA>      <NA>     <NA>      <NA>     <NA>      <NA>
## 93                28.99     20.57    46.76     37.78    40.04      6.85
## 94                 59.6      19.4     10.8       4.2      3.7       3.9
## 95                   60        60     23.2      42.9       14      22.6
## 96                   60        60       60        60       60        60
## 97                   60        60     21.3       3.6      5.5       2.7
## 98                   60      53.2     33.2       5.1      8.5       4.8
## 99                   60        60     36.5      40.7     30.6      13.4
## 100                  60        60     44.9      11.3     23.9      16.6
## 101                  60       5.6       60      48.7      2.9       3.9
## 102                  60      39.9     15.5       4.8     10.8         7
## 103                  60      42.5       60      42.1     21.7      22.7
## 104                34.9      15.2      4.6       3.1      4.3       6.6
## 105                  60      12.3     10.8      32.5     58.7      13.9
## 106                  60        60       60        60     14.6      27.8
## 107                  60        60     45.8      19.9     20.4      25.7
## 108                23.9      27.2       57      12.9      4.8        60
## 109                  60      24.7     31.4      21.7     19.5      25.6
## 110                33.6        60     14.8      23.3     14.3       6.4
## 111                31.2       6.3      6.3       5.6      3.7      12.8
## 112                23.4       8.3     12.2       8.5      3.1         3
## 113                  60        60     53.2      58.5      6.9       3.5
## 114                  60        60        6      14.3     27.2       4.7
## 115                  60        60       60      34.3     21.8      10.3
## 116                  60        60     29.2        60     48.4         4
## 117                  60        60     35.2      17.9     10.8      21.1
## 118                  60        60       60      27.5     12.4       9.9
## 119                  60      25.3       49      12.1     17.7      10.5
## 120                47.1        60     25.2      12.3      3.5       8.9
## 121                  60        60       60      11.3     21.1      10.4
## 122                  60        60     39.6      32.1      5.1       4.7
## 123                  60      52.8       60      50.1     21.4      14.2
## 124                <NA>      <NA>     <NA>      <NA>     <NA>      <NA>
## 125               28.99     20.57    46.76     37.78    40.04      6.85
## 126                59.6      19.4     10.8       4.2      3.7       3.9
## 127                  60        60     23.2      42.9       14      22.6
## 128                  60        60       60        60       60        60
## 129     20.77      58.7        60       60      23.6       60        34
## 130      6.36        60        60       18      11.7       60        60
## 131     20.77        60        60     23.1       5.2     23.8      13.8
## 132      5.68        60        60       60        60       60        60
## 133     11.02        60        60       60      52.5      6.4         3
## 134     18.19      14.3      11.7      5.9       6.4      6.9       7.1
## 135       8.4        60      23.7     19.1      19.5      4.4       7.3
## 136     53.69        60        60       60        60       60        23
## 137       3.2        60        60       60        60     33.8      28.2
## 138     23.36        60        60       60      21.3     10.3       3.2
## 139     39.27        60        60     19.3        60     40.3       7.4
## 140     14.55      48.9      31.8      4.4       4.1     11.8       8.1
## 141      8.23        60      35.1     11.4      31.5      3.3       2.8
## 142     12.58      14.2      24.8       60        60       59        60
## 143      5.76        60      44.9       40      17.1      3.8       3.3
## 144      5.78        60        60     55.2      32.2     19.4       8.6
## 145      4.39      21.8      13.5     11.9         7      4.4      28.5
## 146      7.62      43.2        60     21.1      22.2     29.9       6.6
## 147        12        60        60     26.5      21.2       31      11.7
## 148     14.25        60      49.9       60      40.8     57.8      14.2
## 149      6.83        60      16.2     46.4        57     22.7       5.5
## 150      6.94        60      24.4      4.9      23.3     23.9       5.4
## 151      10.3        60        60       12      16.1     25.9       5.5
## 152      7.31        60        60     58.4        21     21.2      25.8
## 153      4.76        60      40.7     13.6       4.7      5.1      19.7
## 154     19.11        60        60     39.5      12.4     11.6      10.8
## 155      6.05        60      58.6       60      51.1        7      32.3
## 156     27.34        60        60     47.4        60     13.3      13.1
## 157     21.62        60      58.1       60      19.2     12.2      10.4
## 158      4.69        60        60     11.2      20.2     20.2      48.7
## 159     20.77      58.7        60       60      23.6       60        34
## 160      6.36        60        60       18      11.7       60        60
## 161     20.77        60        60     23.1       5.2     23.8      13.8
## 162      5.68        60        60       60        60       60        60
## 163     11.02        60        60       60      52.5      6.4         3
## 164     18.19      14.3      11.7      5.9       6.4      6.9       7.1
## 165       8.4        60      23.7     19.1      19.5      4.4       7.3
## 166     53.69        60        60       60        60       60        23
## 167       3.2        60        60       60        60     33.8      28.2
## 168     23.36        60        60       60      21.3     10.3       3.2
## 169     39.27        60        60     19.3        60     40.3       7.4
## 170     14.55      48.9      31.8      4.4       4.1     11.8       8.1
## 171      8.23        60      35.1     11.4      31.5      3.3       2.8
## 172     12.58      14.2      24.8       60        60       59        60
## 173      5.76        60      44.9       40      17.1      3.8       3.3
## 174      5.78        60        60     55.2      32.2     19.4       8.6
## 175      4.39      21.8      13.5     11.9         7      4.4      28.5
## 176      7.62      43.2        60     21.1      22.2     29.9       6.6
## 177        12        60        60     26.5      21.2       31      11.7
## 178     14.25        60      49.9       60      40.8     57.8      14.2
## 179      6.83        60      16.2     46.4        57     22.7       5.5
## 180      6.94        60      24.4      4.9      23.3     23.9       5.4
## 181      10.3        60        60       12      16.1     25.9       5.5
## 182      7.31        60        60     58.4        21     21.2      25.8
## 183      4.76        60      40.7     13.6       4.7      5.1      19.7
## 184     19.11        60        60     39.5      12.4     11.6      10.8
## 185      6.05        60      58.6       60      51.1        7      32.3
## 186     27.34        60        60     47.4        60     13.3      13.1
## 187     21.62        60      58.1       60      19.2     12.2      10.4
## 188      4.69        60        60     11.2      20.2     20.2      48.7
## 189     20.77      58.7        60       60      23.6       60        34
## 190      6.36        60        60       18      11.7       60        60
## 191     20.77        60        60     23.1       5.2     23.8      13.8
## 192      5.68        60        60       60        60       60        60
## 193     11.02        60        60       60      52.5      6.4         3
## 194     18.19      14.3      11.7      5.9       6.4      6.9       7.1
## 195       8.4        60      23.7     19.1      19.5      4.4       7.3
## 196     53.69        60        60       60        60       60        23
## 197       3.2        60        60       60        60     33.8      28.2
## 198     23.36        60        60       60      21.3     10.3       3.2
## 199     39.27        60        60     19.3        60     40.3       7.4
## 200     14.55      48.9      31.8      4.4       4.1     11.8       8.1
## 201      8.23        60      35.1     11.4      31.5      3.3       2.8
## 202     12.58      14.2      24.8       60        60       59        60
## 203      5.76        60      44.9       40      17.1      3.8       3.3
## 204      5.78        60        60     55.2      32.2     19.4       8.6
## 205      4.39      21.8      13.5     11.9         7      4.4      28.5
## 206      7.62      43.2        60     21.1      22.2     29.9       6.6
## 207        12        60        60     26.5      21.2       31      11.7
## 208     14.25        60      49.9       60      40.8     57.8      14.2
## 209      6.83        60      16.2     46.4        57     22.7       5.5
## 210      6.94        60      24.4      4.9      23.3     23.9       5.4
## 211      10.3        60        60       12      16.1     25.9       5.5
## 212      7.31        60        60     58.4        21     21.2      25.8
## 213      4.76        60      40.7     13.6       4.7      5.1      19.7
## 214     19.11        60        60     39.5      12.4     11.6      10.8
## 215      6.05        60      58.6       60      51.1        7      32.3
## 216     27.34        60        60     47.4        60     13.3      13.1
## 217     21.62        60      58.1       60      19.2     12.2      10.4
## 218      4.69        60        60     11.2      20.2     20.2      48.7
## 219     20.77      58.7        60       60      23.6       60        34
## 220      6.36        60        60       18      11.7       60        60
## 221     20.77        60        60     23.1       5.2     23.8      13.8
## 222      5.68        60        60       60        60       60        60
## 223     11.02        60        60       60      52.5      6.4         3
## 224     18.19      14.3      11.7      5.9       6.4      6.9       7.1
## 225       8.4        60      23.7     19.1      19.5      4.4       7.3
## 226     53.69        60        60       60        60       60        23
## 227       3.2        60        60       60        60     33.8      28.2
## 228     23.36        60        60       60      21.3     10.3       3.2
## 229     39.27        60        60     19.3        60     40.3       7.4
## 230     14.55      48.9      31.8      4.4       4.1     11.8       8.1
## 231      8.23        60      35.1     11.4      31.5      3.3       2.8
## 232     12.58      14.2      24.8       60        60       59        60
## 233      5.76        60      44.9       40      17.1      3.8       3.3
## 234      5.78        60        60     55.2      32.2     19.4       8.6
## 235      4.39      21.8      13.5     11.9         7      4.4      28.5
## 236      7.62      43.2        60     21.1      22.2     29.9       6.6
## 237        12        60        60     26.5      21.2       31      11.7
## 238     14.25        60      49.9       60      40.8     57.8      14.2
## 239      6.83        60      16.2     46.4        57     22.7       5.5
## 240      6.94        60      24.4      4.9      23.3     23.9       5.4
## 241      10.3        60        60       12      16.1     25.9       5.5
## 242      7.31        60        60     58.4        21     21.2      25.8
## 243      4.76        60      40.7     13.6       4.7      5.1      19.7
## 244     19.11        60        60     39.5      12.4     11.6      10.8
## 245      6.05        60      58.6       60      51.1        7      32.3
## 246     27.34        60        60     47.4        60     13.3      13.1
## 247     21.62        60      58.1       60      19.2     12.2      10.4
## 248      4.69        60        60     11.2      20.2     20.2      48.7
## 249     6.173        60    56.094    17.05    49.166  NA_5.89     7.812
## 250     8.618        60        60   57.334    59.008   39.618 NA_17.422
## 251     5.952        60        60       60    59.194 NA_27.59    23.188
## 252     17.36        60        60   53.986    59.628   59.566        60
## 253    10.664    58.016    58.698    28.83      2.17   57.334     4.216
## 254     9.238    18.352  NA_2.108     12.4     7.998   25.668     9.114
## 255     8.618    55.164  NA_0.496   54.358    39.928   25.296   NA_4.65
## 256     8.308    29.202    28.644   18.786     19.84   45.198    11.408
## 257      4.03    14.384     13.95   31.868  NA_4.588 NA_4.712    13.764
## 258     4.752    59.142  NA_3.861   33.363    13.563 NA_3.201     29.37
## 259     6.262    56.652        60       60    28.024   15.376    22.692
## 260     9.672    58.946        60    57.21    31.806    30.69    14.818
## 261     6.882 NA_24.738    34.162    35.03    12.214   55.412    14.384
## 262     9.362        60        60       60        60    58.76      24.8
## 263     7.626        60        60    24.49  NA_6.758    6.882     44.33
## 264    37.696        60        60   58.326 NA_45.322   27.032    33.232
## 265  NA_8.742    34.162    45.198 NA_4.154  NA_5.456 NA_4.588    10.726
## 266    21.452        60        60       60      8.99   16.616     12.71
## 267      4.96    55.428    54.916   40.486    28.086   29.698     9.362
## 268     5.642    28.458     6.634   16.492     13.95   20.584    12.958
## 269     6.634    27.156    10.726    6.572     6.696    8.804     8.556
## 270     5.332    30.566    40.734  NA_4.34    12.524 NA_5.456    13.268
## 271    10.912        60    58.946   57.396    39.742    7.378  NA_4.836
## 272    10.602        60     25.11   14.508    39.184   15.872     8.308
## 273     4.836    57.226        60       60        60   38.068    14.632
## 274     9.362        60        60       60        60       60        60
## 275     7.688     59.69        60 NA_3.658    57.458   35.402    51.708
## 276     10.23        60    57.458   23.746    31.186   12.524    15.252
## 277     3.596 NA_63.054    13.268   58.016        31   18.538  NA_15.19
## 278    39.927        60        60   58.449     59.01   56.106    37.587
## 279      8.37  NA_21.39     7.564   40.424    12.648   32.302    52.018
## 280    22.444        60      56.9   11.532    14.322   10.974    38.192
## 281     6.386     55.66    24.118   59.504      49.6    7.192  NA_7.316
## 282    15.252    28.272    58.574   33.542    12.896   26.598  NA_3.596
## 283     6.014    11.284     4.092    2.666    22.692   10.602     21.39
## 284  NA_7.936        60     58.14       31    18.972    8.928    16.802
## 285  NA_5.766        60  NA_48.98       60    14.074   12.648    21.886
## 286     5.146    57.784 NA_15.872 NA_5.146    29.326   42.904    20.832
## 287     21.39      37.2    19.406   58.636    19.158    9.486     9.114
## 288     7.006     34.41   NA_5.89    45.88      27.9   29.822    12.214
## 289     6.173        60    56.094    17.05    49.166  NA_5.89     7.812
## 290     8.618        60        60   57.334    59.008   39.618 NA_17.422
## 291     5.952        60        60       60    59.194 NA_27.59    23.188
## 292     17.36        60        60   53.986    59.628   59.566        60
## 293    10.664    58.016    58.698    28.83      2.17   57.334     4.216
## 294     9.238    18.352  NA_2.108     12.4     7.998   25.668     9.114
## 295     8.618    55.164  NA_0.496   54.358    39.928   25.296   NA_4.65
## 296     8.308    29.202    28.644   18.786     19.84   45.198    11.408
## 297      4.03    14.384     13.95   31.868  NA_4.588 NA_4.712    13.764
## 298     4.752    59.142  NA_3.861   33.363    13.563 NA_3.201     29.37
## 299     6.262    56.652        60       60    28.024   15.376    22.692
## 300     9.672    58.946        60    57.21    31.806    30.69    14.818
## 301     6.882 NA_24.738    34.162    35.03    12.214   55.412    14.384
## 302     9.362        60        60       60        60    58.76      24.8
## 303     7.626        60        60    24.49  NA_6.758    6.882     44.33
## 304    37.696        60        60   58.326 NA_45.322   27.032    33.232
## 305  NA_8.742    34.162    45.198 NA_4.154  NA_5.456 NA_4.588    10.726
## 306    21.452        60        60       60      8.99   16.616     12.71
## 307      4.96    55.428    54.916   40.486    28.086   29.698     9.362
## 308     5.642    28.458     6.634   16.492     13.95   20.584    12.958
## 309     6.634    27.156    10.726    6.572     6.696    8.804     8.556
## 310     5.332    30.566    40.734  NA_4.34    12.524 NA_5.456    13.268
## 311    10.912        60    58.946   57.396    39.742    7.378  NA_4.836
## 312    10.602        60     25.11   14.508    39.184   15.872     8.308
## 313     4.836    57.226        60       60        60   38.068    14.632
## 314     9.362        60        60       60        60       60        60
## 315     7.688     59.69        60 NA_3.658    57.458   35.402    51.708
## 316     10.23        60    57.458   23.746    31.186   12.524    15.252
## 317     3.596 NA_63.054    13.268   58.016        31   18.538  NA_15.19
## 318    39.927        60        60   58.449     59.01   56.106    37.587
## 319      8.37  NA_21.39     7.564   40.424    12.648   32.302    52.018
## 320    22.444        60      56.9   11.532    14.322   10.974    38.192
## 321     6.386     55.66    24.118   59.504      49.6    7.192  NA_7.316
## 322    15.252    28.272    58.574   33.542    12.896   26.598  NA_3.596
## 323     6.014    11.284     4.092    2.666    22.692   10.602     21.39
## 324  NA_7.936        60     58.14       31    18.972    8.928    16.802
## 325  NA_5.766        60  NA_48.98       60    14.074   12.648    21.886
## 326     5.146    57.784 NA_15.872 NA_5.146    29.326   42.904    20.832
## 327     21.39      37.2    19.406   58.636    19.158    9.486     9.114
## 328     7.006     34.41   NA_5.89    45.88      27.9   29.822    12.214
## 329     6.173        60    56.094    17.05    49.166  NA_5.89     7.812
## 330     8.618        60        60   57.334    59.008   39.618 NA_17.422
## 331     5.952        60        60       60    59.194 NA_27.59    23.188
## 332     17.36        60        60   53.986    59.628   59.566        60
## 333    10.664    58.016    58.698    28.83      2.17   57.334     4.216
## 334     9.238    18.352  NA_2.108     12.4     7.998   25.668     9.114
## 335     8.618    55.164  NA_0.496   54.358    39.928   25.296   NA_4.65
## 336     8.308    29.202    28.644   18.786     19.84   45.198    11.408
## 337      4.03    14.384     13.95   31.868  NA_4.588 NA_4.712    13.764
## 338     4.752    59.142  NA_3.861   33.363    13.563 NA_3.201     29.37
## 339     6.262    56.652        60       60    28.024   15.376    22.692
## 340     9.672    58.946        60    57.21    31.806    30.69    14.818
## 341     6.882 NA_24.738    34.162    35.03    12.214   55.412    14.384
## 342     9.362        60        60       60        60    58.76      24.8
## 343     7.626        60        60    24.49  NA_6.758    6.882     44.33
## 344    37.696        60        60   58.326 NA_45.322   27.032    33.232
## 345  NA_8.742    34.162    45.198 NA_4.154  NA_5.456 NA_4.588    10.726
## 346    21.452        60        60       60      8.99   16.616     12.71
## 347      4.96    55.428    54.916   40.486    28.086   29.698     9.362
## 348     5.642    28.458     6.634   16.492     13.95   20.584    12.958
## 349     6.634    27.156    10.726    6.572     6.696    8.804     8.556
## 350     5.332    30.566    40.734  NA_4.34    12.524 NA_5.456    13.268
## 351    10.912        60    58.946   57.396    39.742    7.378  NA_4.836
## 352    10.602        60     25.11   14.508    39.184   15.872     8.308
## 353     4.836    57.226        60       60        60   38.068    14.632
## 354     9.362        60        60       60        60       60        60
## 355     7.688     59.69        60 NA_3.658    57.458   35.402    51.708
## 356     10.23        60    57.458   23.746    31.186   12.524    15.252
## 357     3.596 NA_63.054    13.268   58.016        31   18.538  NA_15.19
## 358    39.927        60        60   58.449     59.01   56.106    37.587
## 359      8.37  NA_21.39     7.564   40.424    12.648   32.302    52.018
## 360    22.444        60      56.9   11.532    14.322   10.974    38.192
## 361     6.386     55.66    24.118   59.504      49.6    7.192  NA_7.316
## 362    15.252    28.272    58.574   33.542    12.896   26.598  NA_3.596
## 363     6.014    11.284     4.092    2.666    22.692   10.602     21.39
## 364  NA_7.936        60     58.14       31    18.972    8.928    16.802
## 365  NA_5.766        60  NA_48.98       60    14.074   12.648    21.886
## 366     5.146    57.784 NA_15.872 NA_5.146    29.326   42.904    20.832
## 367     21.39      37.2    19.406   58.636    19.158    9.486     9.114
## 368     7.006     34.41   NA_5.89    45.88      27.9   29.822    12.214
## 369     6.173        60    56.094    17.05    49.166  NA_5.89     7.812
## 370     8.618        60        60   57.334    59.008   39.618 NA_17.422
## 371     5.952        60        60       60    59.194 NA_27.59    23.188
## 372     17.36        60        60   53.986    59.628   59.566        60
## 373    10.664    58.016    58.698    28.83      2.17   57.334     4.216
## 374     9.238    18.352  NA_2.108     12.4     7.998   25.668     9.114
## 375     8.618    55.164  NA_0.496   54.358    39.928   25.296   NA_4.65
## 376     8.308    29.202    28.644   18.786     19.84   45.198    11.408
## 377      4.03    14.384     13.95   31.868  NA_4.588 NA_4.712    13.764
## 378     4.752    59.142  NA_3.861   33.363    13.563 NA_3.201     29.37
## 379     6.262    56.652        60       60    28.024   15.376    22.692
## 380     9.672    58.946        60    57.21    31.806    30.69    14.818
## 381     6.882 NA_24.738    34.162    35.03    12.214   55.412    14.384
## 382     9.362        60        60       60        60    58.76      24.8
## 383     7.626        60        60    24.49  NA_6.758    6.882     44.33
## 384    37.696        60        60   58.326 NA_45.322   27.032    33.232
## 385  NA_8.742    34.162    45.198 NA_4.154  NA_5.456 NA_4.588    10.726
## 386    21.452        60        60       60      8.99   16.616     12.71
## 387      4.96    55.428    54.916   40.486    28.086   29.698     9.362
## 388     5.642    28.458     6.634   16.492     13.95   20.584    12.958
## 389     6.634    27.156    10.726    6.572     6.696    8.804     8.556
## 390     5.332    30.566    40.734  NA_4.34    12.524 NA_5.456    13.268
## 391    10.912        60    58.946   57.396    39.742    7.378  NA_4.836
## 392    10.602        60     25.11   14.508    39.184   15.872     8.308
## 393     4.836    57.226        60       60        60   38.068    14.632
## 394     9.362        60        60       60        60       60        60
## 395     7.688     59.69        60 NA_3.658    57.458   35.402    51.708
## 396     10.23        60    57.458   23.746    31.186   12.524    15.252
## 397     3.596 NA_63.054    13.268   58.016        31   18.538  NA_15.19
## 398    39.927        60        60   58.449     59.01   56.106    37.587
## 399      8.37  NA_21.39     7.564   40.424    12.648   32.302    52.018
## 400    22.444        60      56.9   11.532    14.322   10.974    38.192
## 401     6.386     55.66    24.118   59.504      49.6    7.192  NA_7.316
## 402    15.252    28.272    58.574   33.542    12.896   26.598  NA_3.596
## 403     6.014    11.284     4.092    2.666    22.692   10.602     21.39
## 404  NA_7.936        60     58.14       31    18.972    8.928    16.802
## 405  NA_5.766        60  NA_48.98       60    14.074   12.648    21.886
## 406     5.146    57.784 NA_15.872 NA_5.146    29.326   42.904    20.832
## 407     21.39      37.2    19.406   58.636    19.158    9.486     9.114
## 408     7.006     34.41   NA_5.89    45.88      27.9   29.822    12.214
##           test1      test2      test3      test4 avg_fam sum_fam   phase
## 1         24.94      13.03       7.44       9.08    25.5     153 testavg
## 2         34.62      41.24      14.73      28.74    27.5     165 testavg
## 3         15.31      33.44       7.82       5.64    40.2     241 testavg
## 4         13.29       8.58       6.79        3.3    36.1     217 testavg
## 5         27.75      16.84       5.66      11.86    30.2     181 testavg
## 6             7       9.88      16.86      15.48    23.0     138 testavg
## 7         35.42      17.34      12.72      20.34    41.5     249 testavg
## 8          <NA>       <NA>      23.87       11.4    11.4      69 testavg
## 9          7.58      35.76       7.67      13.08    31.4     188 testavg
## 10        45.55       8.28      13.88      13.26    47.1     282 testavg
## 11        11.97       9.93       6.78       6.73    38.6     232 testavg
## 12         37.1      24.34       7.87      44.71    31.0     186 testavg
## 13           60      29.45      35.41       9.88    30.5     183 testavg
## 14         9.06      20.53       8.58      14.72    25.4     152 testavg
## 15        14.22       8.76      15.34       9.49    11.0      66 testavg
## 16         7.16       7.32      15.01       7.26     9.8      58 testavg
## 17        37.22      24.93      16.09        6.8    40.4     242 testavg
## 18        19.04      21.22      12.79       7.87    28.7     172 testavg
## 19           60      26.16      35.51      12.48    41.1     246 testavg
## 20        25.47       8.33       6.91       6.98    43.6     262 testavg
## 21         56.8      15.89       4.07       4.19    34.2     205 testavg
## 22        16.18      20.07       <NA>       <NA>    38.3     230 testavg
## 23        25.27      24.22       6.72      37.59    29.1     175 testavg
## 24        12.87      40.06      34.31      24.09    26.2     157 testavg
## 25        16.84       4.09       <NA>       <NA>    37.1     223 testavg
## 26        17.99      21.39       <NA>       <NA>    33.6     202 testavg
## 27        19.92      18.07       8.63       5.69    43.1     258 testavg
## 28         6.11      25.01      41.03       7.34      NA      NA testavg
## 29        28.38      15.62      16.46       4.27    30.2     181 testavg
## 30         9.32       5.42       3.42      13.29    16.9     102 testavg
## 31        24.65         60         60       12.8    37.1     223 testavg
## 32        39.41         60      19.82         60    60.0     360 testavg
## 33        24.94      13.03       7.44       9.08    25.5     153 testavg
## 34        34.62      41.24      14.73      28.74    27.5     165 testavg
## 35        15.31      33.44       7.82       5.64    40.2     241 testavg
## 36        13.29       8.58       6.79        3.3    36.1     217 testavg
## 37        27.75      16.84       5.66      11.86    30.2     181 testavg
## 38            7       9.88      16.86      15.48    23.0     138 testavg
## 39        35.42      17.34      12.72      20.34    41.5     249 testavg
## 40         <NA>       <NA>      23.87       11.4    11.4      69 testavg
## 41         7.58      35.76       7.67      13.08    31.4     188 testavg
## 42        45.55       8.28      13.88      13.26    47.1     282 testavg
## 43        11.97       9.93       6.78       6.73    38.6     232 testavg
## 44         37.1      24.34       7.87      44.71    31.0     186 testavg
## 45           60      29.45      35.41       9.88    30.5     183 testavg
## 46         9.06      20.53       8.58      14.72    25.4     152 testavg
## 47        14.22       8.76      15.34       9.49    11.0      66 testavg
## 48         7.16       7.32      15.01       7.26     9.8      58 testavg
## 49        37.22      24.93      16.09        6.8    40.4     242 testavg
## 50        19.04      21.22      12.79       7.87    28.7     172 testavg
## 51           60      26.16      35.51      12.48    41.1     246 testavg
## 52        25.47       8.33       6.91       6.98    43.6     262 testavg
## 53         56.8      15.89       4.07       4.19    34.2     205 testavg
## 54        16.18      20.07       <NA>       <NA>    38.3     230 testavg
## 55        25.27      24.22       6.72      37.59    29.1     175 testavg
## 56        12.87      40.06      34.31      24.09    26.2     157 testavg
## 57        16.84       4.09       <NA>       <NA>    37.1     223 testavg
## 58        17.99      21.39       <NA>       <NA>    33.6     202 testavg
## 59        19.92      18.07       8.63       5.69    43.1     258 testavg
## 60         6.11      25.01      41.03       7.34      NA      NA testavg
## 61        28.38      15.62      16.46       4.27    30.2     181 testavg
## 62         9.32       5.42       3.42      13.29    16.9     102 testavg
## 63        24.65         60         60       12.8    37.1     223 testavg
## 64        39.41         60      19.82         60    60.0     360 testavg
## 65        24.94      13.03       7.44       9.08    25.5     153 control
## 66        34.62      41.24      14.73      28.74    27.5     165 control
## 67        15.31      33.44       7.82       5.64    40.2     241 control
## 68        13.29       8.58       6.79        3.3    36.1     217 control
## 69        27.75      16.84       5.66      11.86    30.2     181 control
## 70            7       9.88      16.86      15.48    23.0     138 control
## 71        35.42      17.34      12.72      20.34    41.5     249 control
## 72         <NA>       <NA>      23.87       11.4    11.4      69 control
## 73         7.58      35.76       7.67      13.08    31.4     188 control
## 74        45.55       8.28      13.88      13.26    47.1     282 control
## 75        11.97       9.93       6.78       6.73    38.6     232 control
## 76         37.1      24.34       7.87      44.71    31.0     186 control
## 77           60      29.45      35.41       9.88    30.5     183 control
## 78         9.06      20.53       8.58      14.72    25.4     152 control
## 79        14.22       8.76      15.34       9.49    11.0      66 control
## 80         7.16       7.32      15.01       7.26     9.8      58 control
## 81        37.22      24.93      16.09        6.8    40.4     242 control
## 82        19.04      21.22      12.79       7.87    28.7     172 control
## 83           60      26.16      35.51      12.48    41.1     246 control
## 84        25.47       8.33       6.91       6.98    43.6     262 control
## 85         56.8      15.89       4.07       4.19    34.2     205 control
## 86        16.18      20.07       <NA>       <NA>    38.3     230 control
## 87        25.27      24.22       6.72      37.59    29.1     175 control
## 88        12.87      40.06      34.31      24.09    26.2     157 control
## 89        16.84       4.09       <NA>       <NA>    37.1     223 control
## 90        17.99      21.39       <NA>       <NA>    33.6     202 control
## 91        19.92      18.07       8.63       5.69    43.1     258 control
## 92         6.11      25.01      41.03       7.34      NA      NA control
## 93        28.38      15.62      16.46       4.27    30.2     181 control
## 94         9.32       5.42       3.42      13.29    16.9     102 control
## 95        24.65         60         60       12.8    37.1     223 control
## 96        39.41         60      19.82         60    60.0     360 control
## 97        24.94      13.03       7.44       9.08    25.5     153 control
## 98        34.62      41.24      14.73      28.74    27.5     165 control
## 99        15.31      33.44       7.82       5.64    40.2     241 control
## 100       13.29       8.58       6.79        3.3    36.1     217 control
## 101       27.75      16.84       5.66      11.86    30.2     181 control
## 102           7       9.88      16.86      15.48    23.0     138 control
## 103       35.42      17.34      12.72      20.34    41.5     249 control
## 104        <NA>       <NA>      23.87       11.4    11.4      69 control
## 105        7.58      35.76       7.67      13.08    31.4     188 control
## 106       45.55       8.28      13.88      13.26    47.1     282 control
## 107       11.97       9.93       6.78       6.73    38.6     232 control
## 108        37.1      24.34       7.87      44.71    31.0     186 control
## 109          60      29.45      35.41       9.88    30.5     183 control
## 110        9.06      20.53       8.58      14.72    25.4     152 control
## 111       14.22       8.76      15.34       9.49    11.0      66 control
## 112        7.16       7.32      15.01       7.26     9.8      58 control
## 113       37.22      24.93      16.09        6.8    40.4     242 control
## 114       19.04      21.22      12.79       7.87    28.7     172 control
## 115          60      26.16      35.51      12.48    41.1     246 control
## 116       25.47       8.33       6.91       6.98    43.6     262 control
## 117        56.8      15.89       4.07       4.19    34.2     205 control
## 118       16.18      20.07       <NA>       <NA>    38.3     230 control
## 119       25.27      24.22       6.72      37.59    29.1     175 control
## 120       12.87      40.06      34.31      24.09    26.2     157 control
## 121       16.84       4.09       <NA>       <NA>    37.1     223 control
## 122       17.99      21.39       <NA>       <NA>    33.6     202 control
## 123       19.92      18.07       8.63       5.69    43.1     258 control
## 124        6.11      25.01      41.03       7.34      NA      NA control
## 125       28.38      15.62      16.46       4.27    30.2     181 control
## 126        9.32       5.42       3.42      13.29    16.9     102 control
## 127       24.65         60         60       12.8    37.1     223 control
## 128       39.41         60      19.82         60    60.0     360 control
## 129       47.26         60         60         60    49.4     296 testavg
## 130       55.15       9.18       7.31          5    45.0     270 testavg
## 131 NA - 16.388      16.39       32.5      13.67    31.0     186 testavg
## 132        15.1      16.29       9.45 NA - 3.468    60.0     360 testavg
## 133       31.12       5.81 NA - 3.604 NA - 2.108    40.3     242 testavg
## 134       29.44      31.62       5.41      22.54     8.7      52 testavg
## 135        9.28      21.25       8.36        8.7    22.3     134 testavg
## 136          60         60 NA - 1.292 NA - 1.088    53.8     323 testavg
## 137       39.85         60         60      32.78    50.3     302 testavg
## 138        6.66       6.39  NA - 2.72 NA - 2.788    35.8     215 testavg
## 139   NA - 2.21       8.19      14.01       7.04    41.2     247 testavg
## 140        9.79      11.29       5.75 NA - 3.808    18.2     109 testavg
## 141       40.83      12.95      15.16      12.07    24.0     144 testavg
## 142          60      17.92      44.71      17.58    46.3     278 testavg
## 143        5.13   NA-4.046      19.18         10    28.2     169 testavg
## 144        6.56       4.69      16.32       20.3    39.2     235 testavg
## 145       11.49      40.43       9.66 NA - 2.822    14.5      87 testavg
## 146     NA-3.06         60      45.73      19.89    30.5     183 testavg
## 147       16.22      23.49      10.47       10.2    35.1     210 testavg
## 148       28.46         60      25.51      10.88    47.1     283 testavg
## 149       19.52 NA - 4.182      36.52       39.2    34.6     208 testavg
## 150       14.55       5.17      18.29   Fuss out    23.6     142 testavg
## 151       14.62       8.64      14.52         10    29.9     180 testavg
## 152        3.67      12.41      54.98         60    41.1     246 testavg
## 153        9.96       9.93      14.79       9.93    24.0     144 testavg
## 154        9.66       9.66      17.24      11.93    32.4     194 testavg
## 155       12.04      26.59       5.47      12.51    44.8     269 testavg
## 156       26.04       18.7      34.14      38.62    42.3     254 testavg
## 157       11.73       6.56      16.66       9.01    36.6     220 testavg
## 158       31.82       8.36       9.08      21.22    36.7     220 testavg
## 159       47.26         60         60         60    49.4     296 testavg
## 160       55.15       9.18       7.31          5    45.0     270 testavg
## 161 NA - 16.388      16.39       32.5      13.67    31.0     186 testavg
## 162        15.1      16.29       9.45 NA - 3.468    60.0     360 testavg
## 163       31.12       5.81 NA - 3.604 NA - 2.108    40.3     242 testavg
## 164       29.44      31.62       5.41      22.54     8.7      52 testavg
## 165        9.28      21.25       8.36        8.7    22.3     134 testavg
## 166          60         60 NA - 1.292 NA - 1.088    53.8     323 testavg
## 167       39.85         60         60      32.78    50.3     302 testavg
## 168        6.66       6.39  NA - 2.72 NA - 2.788    35.8     215 testavg
## 169   NA - 2.21       8.19      14.01       7.04    41.2     247 testavg
## 170        9.79      11.29       5.75 NA - 3.808    18.2     109 testavg
## 171       40.83      12.95      15.16      12.07    24.0     144 testavg
## 172          60      17.92      44.71      17.58    46.3     278 testavg
## 173        5.13   NA-4.046      19.18         10    28.2     169 testavg
## 174        6.56       4.69      16.32       20.3    39.2     235 testavg
## 175       11.49      40.43       9.66 NA - 2.822    14.5      87 testavg
## 176     NA-3.06         60      45.73      19.89    30.5     183 testavg
## 177       16.22      23.49      10.47       10.2    35.1     210 testavg
## 178       28.46         60      25.51      10.88    47.1     283 testavg
## 179       19.52 NA - 4.182      36.52       39.2    34.6     208 testavg
## 180       14.55       5.17      18.29   Fuss out    23.6     142 testavg
## 181       14.62       8.64      14.52         10    29.9     180 testavg
## 182        3.67      12.41      54.98         60    41.1     246 testavg
## 183        9.96       9.93      14.79       9.93    24.0     144 testavg
## 184        9.66       9.66      17.24      11.93    32.4     194 testavg
## 185       12.04      26.59       5.47      12.51    44.8     269 testavg
## 186       26.04       18.7      34.14      38.62    42.3     254 testavg
## 187       11.73       6.56      16.66       9.01    36.6     220 testavg
## 188       31.82       8.36       9.08      21.22    36.7     220 testavg
## 189       47.26         60         60         60    49.4     296 control
## 190       55.15       9.18       7.31          5    45.0     270 control
## 191 NA - 16.388      16.39       32.5      13.67    31.0     186 control
## 192        15.1      16.29       9.45 NA - 3.468    60.0     360 control
## 193       31.12       5.81 NA - 3.604 NA - 2.108    40.3     242 control
## 194       29.44      31.62       5.41      22.54     8.7      52 control
## 195        9.28      21.25       8.36        8.7    22.3     134 control
## 196          60         60 NA - 1.292 NA - 1.088    53.8     323 control
## 197       39.85         60         60      32.78    50.3     302 control
## 198        6.66       6.39  NA - 2.72 NA - 2.788    35.8     215 control
## 199   NA - 2.21       8.19      14.01       7.04    41.2     247 control
## 200        9.79      11.29       5.75 NA - 3.808    18.2     109 control
## 201       40.83      12.95      15.16      12.07    24.0     144 control
## 202          60      17.92      44.71      17.58    46.3     278 control
## 203        5.13   NA-4.046      19.18         10    28.2     169 control
## 204        6.56       4.69      16.32       20.3    39.2     235 control
## 205       11.49      40.43       9.66 NA - 2.822    14.5      87 control
## 206     NA-3.06         60      45.73      19.89    30.5     183 control
## 207       16.22      23.49      10.47       10.2    35.1     210 control
## 208       28.46         60      25.51      10.88    47.1     283 control
## 209       19.52 NA - 4.182      36.52       39.2    34.6     208 control
## 210       14.55       5.17      18.29   Fuss out    23.6     142 control
## 211       14.62       8.64      14.52         10    29.9     180 control
## 212        3.67      12.41      54.98         60    41.1     246 control
## 213        9.96       9.93      14.79       9.93    24.0     144 control
## 214        9.66       9.66      17.24      11.93    32.4     194 control
## 215       12.04      26.59       5.47      12.51    44.8     269 control
## 216       26.04       18.7      34.14      38.62    42.3     254 control
## 217       11.73       6.56      16.66       9.01    36.6     220 control
## 218       31.82       8.36       9.08      21.22    36.7     220 control
## 219       47.26         60         60         60    49.4     296 control
## 220       55.15       9.18       7.31          5    45.0     270 control
## 221 NA - 16.388      16.39       32.5      13.67    31.0     186 control
## 222        15.1      16.29       9.45 NA - 3.468    60.0     360 control
## 223       31.12       5.81 NA - 3.604 NA - 2.108    40.3     242 control
## 224       29.44      31.62       5.41      22.54     8.7      52 control
## 225        9.28      21.25       8.36        8.7    22.3     134 control
## 226          60         60 NA - 1.292 NA - 1.088    53.8     323 control
## 227       39.85         60         60      32.78    50.3     302 control
## 228        6.66       6.39  NA - 2.72 NA - 2.788    35.8     215 control
## 229   NA - 2.21       8.19      14.01       7.04    41.2     247 control
## 230        9.79      11.29       5.75 NA - 3.808    18.2     109 control
## 231       40.83      12.95      15.16      12.07    24.0     144 control
## 232          60      17.92      44.71      17.58    46.3     278 control
## 233        5.13   NA-4.046      19.18         10    28.2     169 control
## 234        6.56       4.69      16.32       20.3    39.2     235 control
## 235       11.49      40.43       9.66 NA - 2.822    14.5      87 control
## 236     NA-3.06         60      45.73      19.89    30.5     183 control
## 237       16.22      23.49      10.47       10.2    35.1     210 control
## 238       28.46         60      25.51      10.88    47.1     283 control
## 239       19.52 NA - 4.182      36.52       39.2    34.6     208 control
## 240       14.55       5.17      18.29   Fuss out    23.6     142 control
## 241       14.62       8.64      14.52         10    29.9     180 control
## 242        3.67      12.41      54.98         60    41.1     246 control
## 243        9.96       9.93      14.79       9.93    24.0     144 control
## 244        9.66       9.66      17.24      11.93    32.4     194 control
## 245       12.04      26.59       5.47      12.51    44.8     269 control
## 246       26.04       18.7      34.14      38.62    42.3     254 control
## 247       11.73       6.56      16.66       9.01    36.6     220 control
## 248       31.82       8.36       9.08      21.22    36.7     220 control
## 249       6.138      9.362     10.478       2.79    38.0     190 testavg
## 250        7.75      46.19     32.054     15.066    55.2     276 testavg
## 251      14.694      8.866   NA_9.362    NA_5.89    52.5     262 testavg
## 252      48.174     44.888     22.444     15.934    58.9     353 testavg
## 253       21.39     46.376      6.944     24.924    34.9     209 testavg
## 254      16.678      9.424       8.37     15.128    14.7      74 testavg
## 255       14.26     13.144      7.936     44.578    43.7     175 testavg
## 256       35.34     16.244      5.146      6.014    25.5     153 testavg
## 257      46.872      4.464     45.787       9.24    18.5      74 testavg
## 258      58.548     38.511     13.563     43.263    33.9     135 testavg
## 259      20.026     12.152   NA_3.038   NA_2.232    40.5     243 testavg
## 260        9.92      6.758      9.238      7.378    42.2     253 testavg
## 261      14.012      7.874      7.936      6.386    30.2     151 testavg
## 262       8.432      9.238       5.58     14.078    53.9     324 testavg
## 263      29.264      16.12      51.15     15.934    39.1     196 testavg
## 264      56.156     43.448      8.866     11.346    47.7     239 testavg
## 265       22.32      9.734     10.044     10.602    30.0      90 testavg
## 266      57.458      6.324      5.828     16.678    36.4     218 testavg
## 267      11.222     14.074      5.704     21.948    36.3     218 testavg
## 268      14.632     26.784      13.02      6.882    16.5      99 testavg
## 269      12.214      4.898      8.556      6.448    11.4      69 testavg
## 270      21.452      4.526     25.544     11.098    24.3      97 testavg
## 271       7.998     40.052      6.696     15.996    44.7     223 testavg
## 272      32.302     23.126       8.99     11.408    27.2     163 testavg
## 273      12.772      9.858     15.438     18.228    48.3     290 testavg
## 274       43.71     16.306      8.432     19.034    60.0     360 testavg
## 275      13.888     13.516   NA_7.502   NA_2.418    52.9     264 testavg
## 276      43.462      7.192     31.248     14.694    33.4     200 testavg
## 277   NA_19.406  NA_13.764      8.122       6.51    30.2     121 testavg
## 278       25.41     15.048  NA_31.152   NA_2.673    55.2     331 testavg
## 279      14.694     16.616      13.64       5.89    29.0     145 testavg
## 280       9.796       8.68     14.322     17.236    32.0     192 testavg
## 281       17.98     13.888   NA_9.486   NA_8.246    39.2     196 testavg
## 282        9.92     19.344  NA_20.894  NA_15.004    32.0     160 testavg
## 283       9.424     24.118      8.246      7.068    12.1      73 testavg
## 284      31.744      19.22     12.648      38.75    32.3     194 testavg
## 285       19.84      9.672  NA_18.476   NA_20.77    33.7     169 testavg
## 286       9.176      5.828      3.844       3.41    37.7     151 testavg
## 287       8.618     13.268       8.68     22.382    25.5     153 testavg
## 288   NA_21.018   NA_5.084     28.148      5.766    30.0     150 testavg
## 289       6.138      9.362     10.478       2.79    38.0     190 testavg
## 290        7.75      46.19     32.054     15.066    55.2     276 testavg
## 291      14.694      8.866   NA_9.362    NA_5.89    52.5     262 testavg
## 292      48.174     44.888     22.444     15.934    58.9     353 testavg
## 293       21.39     46.376      6.944     24.924    34.9     209 testavg
## 294      16.678      9.424       8.37     15.128    14.7      74 testavg
## 295       14.26     13.144      7.936     44.578    43.7     175 testavg
## 296       35.34     16.244      5.146      6.014    25.5     153 testavg
## 297      46.872      4.464     45.787       9.24    18.5      74 testavg
## 298      58.548     38.511     13.563     43.263    33.9     135 testavg
## 299      20.026     12.152   NA_3.038   NA_2.232    40.5     243 testavg
## 300        9.92      6.758      9.238      7.378    42.2     253 testavg
## 301      14.012      7.874      7.936      6.386    30.2     151 testavg
## 302       8.432      9.238       5.58     14.078    53.9     324 testavg
## 303      29.264      16.12      51.15     15.934    39.1     196 testavg
## 304      56.156     43.448      8.866     11.346    47.7     239 testavg
## 305       22.32      9.734     10.044     10.602    30.0      90 testavg
## 306      57.458      6.324      5.828     16.678    36.4     218 testavg
## 307      11.222     14.074      5.704     21.948    36.3     218 testavg
## 308      14.632     26.784      13.02      6.882    16.5      99 testavg
## 309      12.214      4.898      8.556      6.448    11.4      69 testavg
## 310      21.452      4.526     25.544     11.098    24.3      97 testavg
## 311       7.998     40.052      6.696     15.996    44.7     223 testavg
## 312      32.302     23.126       8.99     11.408    27.2     163 testavg
## 313      12.772      9.858     15.438     18.228    48.3     290 testavg
## 314       43.71     16.306      8.432     19.034    60.0     360 testavg
## 315      13.888     13.516   NA_7.502   NA_2.418    52.9     264 testavg
## 316      43.462      7.192     31.248     14.694    33.4     200 testavg
## 317   NA_19.406  NA_13.764      8.122       6.51    30.2     121 testavg
## 318       25.41     15.048  NA_31.152   NA_2.673    55.2     331 testavg
## 319      14.694     16.616      13.64       5.89    29.0     145 testavg
## 320       9.796       8.68     14.322     17.236    32.0     192 testavg
## 321       17.98     13.888   NA_9.486   NA_8.246    39.2     196 testavg
## 322        9.92     19.344  NA_20.894  NA_15.004    32.0     160 testavg
## 323       9.424     24.118      8.246      7.068    12.1      73 testavg
## 324      31.744      19.22     12.648      38.75    32.3     194 testavg
## 325       19.84      9.672  NA_18.476   NA_20.77    33.7     169 testavg
## 326       9.176      5.828      3.844       3.41    37.7     151 testavg
## 327       8.618     13.268       8.68     22.382    25.5     153 testavg
## 328   NA_21.018   NA_5.084     28.148      5.766    30.0     150 testavg
## 329       6.138      9.362     10.478       2.79    38.0     190 control
## 330        7.75      46.19     32.054     15.066    55.2     276 control
## 331      14.694      8.866   NA_9.362    NA_5.89    52.5     262 control
## 332      48.174     44.888     22.444     15.934    58.9     353 control
## 333       21.39     46.376      6.944     24.924    34.9     209 control
## 334      16.678      9.424       8.37     15.128    14.7      74 control
## 335       14.26     13.144      7.936     44.578    43.7     175 control
## 336       35.34     16.244      5.146      6.014    25.5     153 control
## 337      46.872      4.464     45.787       9.24    18.5      74 control
## 338      58.548     38.511     13.563     43.263    33.9     135 control
## 339      20.026     12.152   NA_3.038   NA_2.232    40.5     243 control
## 340        9.92      6.758      9.238      7.378    42.2     253 control
## 341      14.012      7.874      7.936      6.386    30.2     151 control
## 342       8.432      9.238       5.58     14.078    53.9     324 control
## 343      29.264      16.12      51.15     15.934    39.1     196 control
## 344      56.156     43.448      8.866     11.346    47.7     239 control
## 345       22.32      9.734     10.044     10.602    30.0      90 control
## 346      57.458      6.324      5.828     16.678    36.4     218 control
## 347      11.222     14.074      5.704     21.948    36.3     218 control
## 348      14.632     26.784      13.02      6.882    16.5      99 control
## 349      12.214      4.898      8.556      6.448    11.4      69 control
## 350      21.452      4.526     25.544     11.098    24.3      97 control
## 351       7.998     40.052      6.696     15.996    44.7     223 control
## 352      32.302     23.126       8.99     11.408    27.2     163 control
## 353      12.772      9.858     15.438     18.228    48.3     290 control
## 354       43.71     16.306      8.432     19.034    60.0     360 control
## 355      13.888     13.516   NA_7.502   NA_2.418    52.9     264 control
## 356      43.462      7.192     31.248     14.694    33.4     200 control
## 357   NA_19.406  NA_13.764      8.122       6.51    30.2     121 control
## 358       25.41     15.048  NA_31.152   NA_2.673    55.2     331 control
## 359      14.694     16.616      13.64       5.89    29.0     145 control
## 360       9.796       8.68     14.322     17.236    32.0     192 control
## 361       17.98     13.888   NA_9.486   NA_8.246    39.2     196 control
## 362        9.92     19.344  NA_20.894  NA_15.004    32.0     160 control
## 363       9.424     24.118      8.246      7.068    12.1      73 control
## 364      31.744      19.22     12.648      38.75    32.3     194 control
## 365       19.84      9.672  NA_18.476   NA_20.77    33.7     169 control
## 366       9.176      5.828      3.844       3.41    37.7     151 control
## 367       8.618     13.268       8.68     22.382    25.5     153 control
## 368   NA_21.018   NA_5.084     28.148      5.766    30.0     150 control
## 369       6.138      9.362     10.478       2.79    38.0     190 control
## 370        7.75      46.19     32.054     15.066    55.2     276 control
## 371      14.694      8.866   NA_9.362    NA_5.89    52.5     262 control
## 372      48.174     44.888     22.444     15.934    58.9     353 control
## 373       21.39     46.376      6.944     24.924    34.9     209 control
## 374      16.678      9.424       8.37     15.128    14.7      74 control
## 375       14.26     13.144      7.936     44.578    43.7     175 control
## 376       35.34     16.244      5.146      6.014    25.5     153 control
## 377      46.872      4.464     45.787       9.24    18.5      74 control
## 378      58.548     38.511     13.563     43.263    33.9     135 control
## 379      20.026     12.152   NA_3.038   NA_2.232    40.5     243 control
## 380        9.92      6.758      9.238      7.378    42.2     253 control
## 381      14.012      7.874      7.936      6.386    30.2     151 control
## 382       8.432      9.238       5.58     14.078    53.9     324 control
## 383      29.264      16.12      51.15     15.934    39.1     196 control
## 384      56.156     43.448      8.866     11.346    47.7     239 control
## 385       22.32      9.734     10.044     10.602    30.0      90 control
## 386      57.458      6.324      5.828     16.678    36.4     218 control
## 387      11.222     14.074      5.704     21.948    36.3     218 control
## 388      14.632     26.784      13.02      6.882    16.5      99 control
## 389      12.214      4.898      8.556      6.448    11.4      69 control
## 390      21.452      4.526     25.544     11.098    24.3      97 control
## 391       7.998     40.052      6.696     15.996    44.7     223 control
## 392      32.302     23.126       8.99     11.408    27.2     163 control
## 393      12.772      9.858     15.438     18.228    48.3     290 control
## 394       43.71     16.306      8.432     19.034    60.0     360 control
## 395      13.888     13.516   NA_7.502   NA_2.418    52.9     264 control
## 396      43.462      7.192     31.248     14.694    33.4     200 control
## 397   NA_19.406  NA_13.764      8.122       6.51    30.2     121 control
## 398       25.41     15.048  NA_31.152   NA_2.673    55.2     331 control
## 399      14.694     16.616      13.64       5.89    29.0     145 control
## 400       9.796       8.68     14.322     17.236    32.0     192 control
## 401       17.98     13.888   NA_9.486   NA_8.246    39.2     196 control
## 402        9.92     19.344  NA_20.894  NA_15.004    32.0     160 control
## 403       9.424     24.118      8.246      7.068    12.1      73 control
## 404      31.744      19.22     12.648      38.75    32.3     194 control
## 405       19.84      9.672  NA_18.476   NA_20.77    33.7     169 control
## 406       9.176      5.828      3.844       3.41    37.7     151 control
## 407       8.618     13.268       8.68     22.382    25.5     153 control
## 408   NA_21.018   NA_5.084     28.148      5.766    30.0     150 control
##        type look loglook agegroup
## 1     lower 16.2     2.8  younger
## 2     lower 24.7     3.2  younger
## 3     lower 19.5     3.0  younger
## 4     lower 10.0     2.3  younger
## 5     lower 14.3     2.7  younger
## 6     lower 11.9     2.5  younger
## 7     lower 24.1     3.2  younger
## 8     lower 23.9     3.2  younger
## 9     lower 24.4     3.2  younger
## 10    lower 29.7     3.4  younger
## 11    lower  9.4     2.2  younger
## 12    lower 22.5     3.1  younger
## 13    lower 19.7     3.0  younger
## 14    lower 17.6     2.9  younger
## 15    lower 14.8     2.7  younger
## 16    lower 11.1     2.4  younger
## 17    lower 15.9     2.8  younger
## 18    lower 14.6     2.7  younger
## 19    lower 19.3     3.0  younger
## 20    lower  7.7     2.0  younger
## 21    lower 30.4     3.4  younger
## 22    lower 20.1     3.0  younger
## 23    lower 16.0     2.8  younger
## 24    lower 32.1     3.5  younger
## 25    lower  4.1     1.4  younger
## 26    lower 21.4     3.1  younger
## 27    lower 14.3     2.7  younger
## 28    lower 16.2     2.8  younger
## 29    lower  9.9     2.3  younger
## 30    lower  6.4     1.9  younger
## 31    lower 42.3     3.7  younger
## 32    lower 60.0     4.1  younger
## 33   higher 11.1     2.4  younger
## 34   higher 35.0     3.6  younger
## 35   higher 11.6     2.4  younger
## 36   higher  5.9     1.8  younger
## 37   higher 16.7     2.8  younger
## 38   higher 12.7     2.5  younger
## 39   higher 18.8     2.9  younger
## 40   higher 11.4     2.4  younger
## 41   higher  7.6     2.0  younger
## 42   higher 10.8     2.4  younger
## 43   higher  8.3     2.1  younger
## 44   higher 34.5     3.5  younger
## 45   higher 47.7     3.9  younger
## 46   higher  8.8     2.2  younger
## 47   higher  9.1     2.2  younger
## 48   higher  7.3     2.0  younger
## 49   higher 26.7     3.3  younger
## 50   higher 15.9     2.8  younger
## 51   higher 47.8     3.9  younger
## 52   higher 16.2     2.8  younger
## 53   higher 10.0     2.3  younger
## 54   higher 16.2     2.8  younger
## 55   higher 30.9     3.4  younger
## 56   higher 23.6     3.2  younger
## 57   higher 16.8     2.8  younger
## 58   higher 18.0     2.9  younger
## 59   higher 11.9     2.5  younger
## 60   higher 23.6     3.2  younger
## 61   higher 22.4     3.1  younger
## 62   higher  9.4     2.2  younger
## 63   higher 36.4     3.6  younger
## 64   higher 29.6     3.4  younger
## 65  shallow   NA      NA  younger
## 66  shallow   NA      NA  younger
## 67  shallow   NA      NA  younger
## 68  shallow   NA      NA  younger
## 69  shallow   NA      NA  younger
## 70  shallow   NA      NA  younger
## 71  shallow   NA      NA  younger
## 72  shallow   NA      NA  younger
## 73  shallow   NA      NA  younger
## 74  shallow   NA      NA  younger
## 75  shallow   NA      NA  younger
## 76  shallow   NA      NA  younger
## 77  shallow   NA      NA  younger
## 78  shallow   NA      NA  younger
## 79  shallow   NA      NA  younger
## 80  shallow   NA      NA  younger
## 81  shallow   NA      NA  younger
## 82  shallow   NA      NA  younger
## 83  shallow   NA      NA  younger
## 84  shallow   NA      NA  younger
## 85  shallow   NA      NA  younger
## 86  shallow   NA      NA  younger
## 87  shallow   NA      NA  younger
## 88  shallow   NA      NA  younger
## 89  shallow   NA      NA  younger
## 90  shallow   NA      NA  younger
## 91  shallow   NA      NA  younger
## 92  shallow   NA      NA  younger
## 93  shallow   NA      NA  younger
## 94  shallow   NA      NA  younger
## 95  shallow   NA      NA  younger
## 96  shallow   NA      NA  younger
## 97     deep   NA      NA  younger
## 98     deep   NA      NA  younger
## 99     deep   NA      NA  younger
## 100    deep   NA      NA  younger
## 101    deep   NA      NA  younger
## 102    deep   NA      NA  younger
## 103    deep   NA      NA  younger
## 104    deep   NA      NA  younger
## 105    deep   NA      NA  younger
## 106    deep   NA      NA  younger
## 107    deep   NA      NA  younger
## 108    deep   NA      NA  younger
## 109    deep   NA      NA  younger
## 110    deep   NA      NA  younger
## 111    deep   NA      NA  younger
## 112    deep   NA      NA  younger
## 113    deep   NA      NA  younger
## 114    deep   NA      NA  younger
## 115    deep   NA      NA  younger
## 116    deep   NA      NA  younger
## 117    deep   NA      NA  younger
## 118    deep   NA      NA  younger
## 119    deep   NA      NA  younger
## 120    deep   NA      NA  younger
## 121    deep   NA      NA  younger
## 122    deep   NA      NA  younger
## 123    deep   NA      NA  younger
## 124    deep   NA      NA  younger
## 125    deep   NA      NA  younger
## 126    deep   NA      NA  younger
## 127    deep   NA      NA  younger
## 128    deep   NA      NA  younger
## 129   lower 53.6     4.0  younger
## 130   lower 31.2     3.4  younger
## 131   lower 15.0     2.7  younger
## 132   lower 12.3     2.5  younger
## 133   lower  5.8     1.8  younger
## 134   lower 17.4     2.9  younger
## 135   lower  8.8     2.2  younger
## 136   lower 60.0     4.1  younger
## 137   lower 46.4     3.8  younger
## 138   lower  6.7     1.9  younger
## 139   lower  7.6     2.0  younger
## 140   lower  7.8     2.1  younger
## 141   lower 12.5     2.5  younger
## 142   lower 17.8     2.9  younger
## 143   lower 12.2     2.5  younger
## 144   lower 12.5     2.5  younger
## 145   lower 10.6     2.4  younger
## 146   lower 40.0     3.7  younger
## 147   lower 16.9     2.8  younger
## 148   lower 35.4     3.6  younger
## 149   lower 28.0     3.3  younger
## 150   lower  5.2     1.6  younger
## 151   lower 14.6     2.7  younger
## 152   lower 29.3     3.4  younger
## 153   lower 12.4     2.5  younger
## 154   lower 10.8     2.4  younger
## 155   lower 19.6     3.0  younger
## 156   lower 30.1     3.4  younger
## 157   lower 14.2     2.7  younger
## 158   lower 14.8     2.7  younger
## 159  higher 60.0     4.1  younger
## 160  higher  7.1     2.0  younger
## 161  higher 32.5     3.5  younger
## 162  higher 16.3     2.8  younger
## 163  higher 31.1     3.4  younger
## 164  higher 27.1     3.3  younger
## 165  higher 15.0     2.7  younger
## 166  higher 60.0     4.1  younger
## 167  higher 49.9     3.9  younger
## 168  higher  6.4     1.9  younger
## 169  higher 14.0     2.6  younger
## 170  higher 11.3     2.4  younger
## 171  higher 28.0     3.3  younger
## 172  higher 52.4     4.0  younger
## 173  higher 10.0     2.3  younger
## 174  higher 11.4     2.4  younger
## 175  higher 40.4     3.7  younger
## 176  higher 45.7     3.8  younger
## 177  higher 13.3     2.6  younger
## 178  higher 27.0     3.3  younger
## 179  higher 39.2     3.7  younger
## 180  higher 16.4     2.8  younger
## 181  higher  9.3     2.2  younger
## 182  higher 36.2     3.6  younger
## 183  higher  9.9     2.3  younger
## 184  higher 13.4     2.6  younger
## 185  higher  8.8     2.2  younger
## 186  higher 28.7     3.4  younger
## 187  higher  7.8     2.1  younger
## 188  higher 20.4     3.0  younger
## 189 shallow 20.8     3.0  younger
## 190 shallow  6.4     1.9  younger
## 191 shallow 14.6     2.7  younger
## 192 shallow  5.7     1.7  younger
## 193 shallow 15.0     2.7  younger
## 194 shallow 18.2     2.9  younger
## 195 shallow  8.4     2.1  younger
## 196 shallow 53.7     4.0  younger
## 197 shallow 22.0     3.1  younger
## 198 shallow 23.4     3.2  younger
## 199 shallow  9.6     2.3  younger
## 200 shallow 14.6     2.7  younger
## 201 shallow 19.3     3.0  younger
## 202 shallow 13.0     2.6  younger
## 203 shallow  5.8     1.8  younger
## 204 shallow 11.3     2.4  younger
## 205 shallow  4.4     1.5  younger
## 206 shallow  9.3     2.2  younger
## 207 shallow 19.9     3.0  younger
## 208 shallow 26.0     3.3  younger
## 209 shallow  6.8     1.9  younger
## 210 shallow 11.9     2.5  younger
## 211 shallow 10.3     2.3  younger
## 212 shallow  7.3     2.0  younger
## 213 shallow  4.8     1.6  younger
## 214 shallow 10.9     2.4  younger
## 215 shallow 13.5     2.6  younger
## 216 shallow 27.3     3.3  younger
## 217 shallow 21.6     3.1  younger
## 218 shallow  4.6     1.5  younger
## 219    deep 26.1     3.3  younger
## 220    deep  4.6     1.5  younger
## 221    deep 20.8     3.0  younger
## 222    deep  5.4     1.7  younger
## 223    deep 11.0     2.4  younger
## 224    deep 10.2     2.3  younger
## 225    deep  9.8     2.3  younger
## 226    deep 17.0     2.8  younger
## 227    deep  3.2     1.2  younger
## 228    deep 10.0     2.3  younger
## 229    deep 39.3     3.7  younger
## 230    deep 13.2     2.6  younger
## 231    deep  8.2     2.1  younger
## 232    deep 12.6     2.5  younger
## 233    deep 10.5     2.3  younger
## 234    deep  5.8     1.8  younger
## 235    deep  5.8     1.8  younger
## 236    deep  7.6     2.0  younger
## 237    deep 12.0     2.5  younger
## 238    deep 14.2     2.7  younger
## 239    deep 17.6     2.9  younger
## 240    deep  6.9     1.9  younger
## 241    deep 16.3     2.8  younger
## 242    deep  5.9     1.8  younger
## 243    deep 19.2     3.0  younger
## 244    deep 19.1     3.0  younger
## 245    deep  6.0     1.8  younger
## 246    deep 16.1     2.8  younger
## 247    deep 22.9     3.1  younger
## 248    deep  4.7     1.5  younger
## 249   lower  6.1     1.8  younger
## 250   lower 30.6     3.4  younger
## 251   lower  8.9     2.2  younger
## 252   lower 30.4     3.4  younger
## 253   lower 35.6     3.6  younger
## 254   lower 12.3     2.5  younger
## 255   lower 28.9     3.4  younger
## 256   lower 11.1     2.4  younger
## 257   lower  6.9     1.9  younger
## 258   lower 40.9     3.7  younger
## 259   lower 20.0     3.0  younger
## 260   lower  9.6     2.3  younger
## 261   lower 11.0     2.4  younger
## 262   lower  7.0     1.9  younger
## 263   lower 40.2     3.7  younger
## 264   lower 32.5     3.5  younger
## 265   lower 16.2     2.8  younger
## 266   lower 31.6     3.5  younger
## 267   lower  8.5     2.1  younger
## 268   lower 13.8     2.6  younger
## 269   lower 10.4     2.3  younger
## 270   lower 23.5     3.2  younger
## 271   lower  7.3     2.0  younger
## 272   lower 20.6     3.0  younger
## 273   lower 14.1     2.6  younger
## 274   lower 26.1     3.3  younger
## 275   lower 13.9     2.6  younger
## 276   lower 37.4     3.6  younger
## 277   lower  8.1     2.1  younger
## 278   lower 25.4     3.2  younger
## 279   lower 11.3     2.4  younger
## 280   lower 13.0     2.6  younger
## 281   lower 13.9     2.6  younger
## 282   lower 19.3     3.0  younger
## 283   lower 15.6     2.7  younger
## 284   lower 29.0     3.4  younger
## 285   lower  9.7     2.3  younger
## 286   lower  4.6     1.5  younger
## 287   lower 17.8     2.9  younger
## 288   lower  5.8     1.8  younger
## 289  higher  8.3     2.1  younger
## 290  higher 19.9     3.0  younger
## 291  higher 14.7     2.7  younger
## 292  higher 35.3     3.6  younger
## 293  higher 14.2     2.7  younger
## 294  higher 12.5     2.5  younger
## 295  higher 11.1     2.4  younger
## 296  higher 20.2     3.0  younger
## 297  higher 46.3     3.8  younger
## 298  higher 36.1     3.6  younger
## 299  higher 12.2     2.5  younger
## 300  higher  7.1     2.0  younger
## 301  higher  7.1     2.0  younger
## 302  higher 11.7     2.5  younger
## 303  higher 16.0     2.8  younger
## 304  higher 27.4     3.3  younger
## 305  higher 10.2     2.3  younger
## 306  higher 11.5     2.4  younger
## 307  higher 18.0     2.9  younger
## 308  higher 16.8     2.8  younger
## 309  higher  5.7     1.7  younger
## 310  higher  7.8     2.1  younger
## 311  higher 28.0     3.3  younger
## 312  higher 17.3     2.8  younger
## 313  higher 14.0     2.6  younger
## 314  higher 17.7     2.9  younger
## 315  higher 13.5     2.6  younger
## 316  higher 10.9     2.4  younger
## 317  higher  6.5     1.9  younger
## 318  higher 15.0     2.7  younger
## 319  higher 14.2     2.7  younger
## 320  higher 12.1     2.5  younger
## 321  higher 18.0     2.9  younger
## 322  higher  9.9     2.3  younger
## 323  higher  8.8     2.2  younger
## 324  higher 22.2     3.1  younger
## 325  higher 19.8     3.0  younger
## 326  higher  6.5     1.9  younger
## 327  higher  8.6     2.2  younger
## 328  higher 28.1     3.3  younger
## 329 shallow  6.2     1.8  younger
## 330 shallow  8.6     2.2  younger
## 331 shallow  6.0     1.8  younger
## 332 shallow 17.4     2.9  younger
## 333 shallow 10.7     2.4  younger
## 334 shallow  9.2     2.2  younger
## 335 shallow  8.6     2.2  younger
## 336 shallow  8.3     2.1  younger
## 337 shallow  4.0     1.4  younger
## 338 shallow  4.8     1.6  younger
## 339 shallow 21.8     3.1  younger
## 340 shallow 26.4     3.3  younger
## 341 shallow 12.1     2.5  younger
## 342 shallow  7.1     2.0  younger
## 343 shallow 16.1     2.8  younger
## 344 shallow 12.8     2.5  younger
## 345 shallow   NA      NA  younger
## 346 shallow 23.0     3.1  younger
## 347 shallow 10.3     2.3  younger
## 348 shallow  9.8     2.3  younger
## 349 shallow  6.3     1.8  younger
## 350 shallow  6.6     1.9  younger
## 351 shallow 11.3     2.4  younger
## 352 shallow 23.1     3.1  younger
## 353 shallow 11.6     2.4  younger
## 354 shallow 21.9     3.1  younger
## 355 shallow 18.0     2.9  younger
## 356 shallow  9.9     2.3  younger
## 357 shallow 12.4     2.5  younger
## 358 shallow 19.1     3.0  younger
## 359 shallow  8.4     2.1  younger
## 360 shallow 22.4     3.1  younger
## 361 shallow  6.4     1.9  younger
## 362 shallow 15.3     2.7  younger
## 363 shallow  6.0     1.8  younger
## 364 shallow   NA      NA  younger
## 365 shallow   NA      NA  younger
## 366 shallow  5.1     1.6  younger
## 367 shallow 21.4     3.1  younger
## 368 shallow  7.0     1.9  younger
## 369    deep 21.1     3.0  younger
## 370    deep  9.5     2.3  younger
## 371    deep  5.0     1.6  younger
## 372    deep 13.0     2.6  younger
## 373    deep 20.1     3.0  younger
## 374    deep 11.7     2.5  younger
## 375    deep 31.4     3.4  younger
## 376    deep  8.4     2.1  younger
## 377    deep 14.1     2.6  younger
## 378    deep  4.0     1.4  younger
## 379    deep  6.3     1.8  younger
## 380    deep  9.7     2.3  younger
## 381    deep  6.9     1.9  younger
## 382    deep  9.4     2.2  younger
## 383    deep  7.6     2.0  younger
## 384    deep 37.7     3.6  younger
## 385    deep   NA      NA  younger
## 386    deep 21.5     3.1  younger
## 387    deep  5.0     1.6  younger
## 388    deep  5.6     1.7  younger
## 389    deep  6.6     1.9  younger
## 390    deep  5.3     1.7  younger
## 391    deep 10.9     2.4  younger
## 392    deep 10.6     2.4  younger
## 393    deep  4.8     1.6  younger
## 394    deep  9.4     2.2  younger
## 395    deep  7.7     2.0  younger
## 396    deep 10.2     2.3  younger
## 397    deep  3.6     1.3  younger
## 398    deep 39.9     3.7  younger
## 399    deep 19.1     3.0  younger
## 400    deep 24.4     3.2  younger
## 401    deep 14.4     2.7  younger
## 402    deep  9.7     2.3  younger
## 403    deep 17.1     2.8  younger
## 404    deep  6.9     1.9  younger
## 405    deep   NA      NA  younger
## 406    deep   NA      NA  younger
## 407    deep 11.8     2.5  younger
## 408    deep 10.9     2.4  younger
risk.avg <- long.avg %>%
  filter(cost == "Risk") %>%
  mutate(task = as.factor(
    case_when(experiment == "RISK13" ~ "infer.value",
              (experiment == "MR13" | experiment == "MR2") ~ "min.risk")))

op <- options(contrasts = c("contr.treatment", "contr.poly")) # treatment contrasts
# function for identifying influential observations, and then returning a new model without them
# INPUTS: model = model name, data = dataset, and subj = column heading for observations
# OUTPUT: model excluding influential subjects
exclude.cooks <- function(model, data, subj) {
  cooks <- cooks.distance(influence(model, subj))
  cutoff <- 4/length(unique(data$subj))
  new.model <- exclude.influence(model, grouping = subj, level=data[which(cooks > cutoff),]$subj)
  return(new.model)
}

# function that computes CIs and returns them in df
gen.ci <- function(model) {
  df <- data.frame(confint(model))
  names(df) <- c("lower", "upper")
  return(df)
}

# function that converts model summary to df
gen.m <- function(model) {
  df <- data.frame(coef(summary(model)))
  names(df) <- c("est", "se", "df", "t", "p")
  return(df)
}

# function that returns column of standardized betas from lmer model
gen.beta <- function(model) {
  f <- data.frame(fixef(model))
  colnames(f) <- "beta"
  return(f)
}
# function that returns age info and number of female infants in a dataset

info <- function(longdata) {
  longdata %>% 
  group_by(subj) %>%
  filter(row_number()==1) %>%
  ungroup() %>%
  summarize(mean = mean(agem), min=range(agem)[1], max=range(agem)[2], f=sum(sex=="f"), n=length(unique(subj)))
}

## Retrieved from : http://www.cookbook-r.com/Graphs/Plotting_means_and_error_bars_(ggplot2)/#error-bars-for-within-subjects-variables
## Gives count, mean, standard deviation, standard error of the mean, and confidence interval (default 95%).
##   data: a data frame.
##   measurevar: the name of a column that contains the variable to be summariezed
##   groupvars: a vector containing names of columns that contain grouping variables
##   na.rm: a boolean that indicates whether to ignore NA's
##   conf.interval: the percent range of the confidence interval (default is 95%)
summarySE <- function(data=NULL, measurevar, groupvars=NULL, na.rm=TRUE,
                      conf.interval=.95, .drop=TRUE) {
  library(plyr)
  
  # New version of length which can handle NA's: if na.rm==T, don't count them
  length2 <- function (x, na.rm=FALSE) {
    if (na.rm) sum(!is.na(x))
    else       length(x)
  }
  
  # This does the summary. For each group's data frame, return a vector with
  # N, mean, and sd
  datac <- ddply(data, groupvars, .drop=.drop,
                 .fun = function(xx, col) {
                   c(N    = length2(xx[[col]], na.rm=na.rm),
                     mean = mean   (xx[[col]], na.rm=na.rm),
                     sd   = sd     (xx[[col]], na.rm=na.rm)
                   )
                 },
                 measurevar
  )
  
  # Rename the "mean" column    
  datac <- plyr::rename(datac, c("mean" = measurevar))
  
  datac$se <- datac$sd / sqrt(datac$N)  # Calculate standard error of the mean
  
  # Confidence interval multiplier for standard error
  # Calculate t-statistic for confidence interval: 
  # e.g., if conf.interval is .95, use .975 (above/below), and use df=N-1
  ciMult <- qt(conf.interval/2 + .5, datac$N-1)
  datac$ci <- datac$se * ciMult
  
  return(datac)
}
## Norms the data within specified groups in a data frame; it normalizes each
## subject (identified by idvar) so that they have the same mean, within each group
## specified by betweenvars.
##   data: a data frame.
##   idvar: the name of a column that identifies each subject (or matched subjects)
##   measurevar: the name of a column that contains the variable to be summariezed
##   betweenvars: a vector containing names of columns that are between-subjects variables
##   na.rm: a boolean that indicates whether to ignore NA's
normDataWithin <- function(data=NULL, idvar, measurevar, betweenvars=NULL,
                           na.rm=TRUE, .drop=TRUE) {
  library(plyr)
  
  # Measure var on left, idvar + between vars on right of formula.
  data.subjMean <- ddply(data, c(idvar, betweenvars), .drop=.drop,
                         .fun = function(xx, col, na.rm) {
                           c(subjMean = mean(xx[,col], na.rm=na.rm))
                         },
                         measurevar,
                         na.rm
  )
  
  # Put the subject means with original data
  data <- merge(data, data.subjMean)
  
  # Get the normalized data in a new column
  measureNormedVar <- paste(measurevar, "_norm", sep="")
  data[,measureNormedVar] <- data[,measurevar] - data[,"subjMean"] +
    mean(data[,measurevar], na.rm=na.rm)
  
  # Remove this subject mean column
  data$subjMean <- NULL
  
  return(data)
}

## Summarizes data, handling within-subjects variables by removing inter-subject variability.
## It will still work if there are no within-S variables.
## Gives count, un-normed mean, normed mean (with same between-group mean),
##   standard deviation, standard error of the mean, and confidence interval.
## If there are within-subject variables, calculate adjusted values using method from Morey (2008).
##   data: a data frame.
##   measurevar: the name of a column that contains the variable to be summariezed
##   betweenvars: a vector containing names of columns that are between-subjects variables
##   withinvars: a vector containing names of columns that are within-subjects variables
##   idvar: the name of a column that identifies each subject (or matched subjects)
##   na.rm: a boolean that indicates whether to ignore NA's
##   conf.interval: the percent range of the confidence interval (default is 95%)
summarySEwithin <- function(data=NULL, measurevar, betweenvars=NULL, withinvars=NULL,
                            idvar=NULL, na.rm=TRUE, conf.interval=.95, .drop=TRUE) {
  
  # Ensure that the betweenvars and withinvars are factors
  factorvars <- vapply(data[, c(betweenvars, withinvars), drop=FALSE],
                       FUN=is.factor, FUN.VALUE=logical(1))
  
  if (!all(factorvars)) {
    nonfactorvars <- names(factorvars)[!factorvars]
    message("Automatically converting the following non-factors to factors: ",
            paste(nonfactorvars, collapse = ", "))
    data[nonfactorvars] <- lapply(data[nonfactorvars], factor)
  }
  
  # Get the means from the un-normed data
  datac <- summarySE(data, measurevar, groupvars=c(betweenvars, withinvars),
                     na.rm=na.rm, conf.interval=conf.interval, .drop=.drop)
  
  # Drop all the unused columns (these will be calculated with normed data)
  datac$sd <- NULL
  datac$se <- NULL
  datac$ci <- NULL
  
  # Norm each subject's data
  ndata <- normDataWithin(data, idvar, measurevar, betweenvars, na.rm, .drop=.drop)
  
  # This is the name of the new column
  measurevar_n <- paste(measurevar, "_norm", sep="")
  
  # Collapse the normed data - now we can treat between and within vars the same
  ndatac <- summarySE(ndata, measurevar_n, groupvars=c(betweenvars, withinvars),
                      na.rm=na.rm, conf.interval=conf.interval, .drop=.drop)
  
  # Apply correction from Morey (2008) to the standard error and confidence interval
  #  Get the product of the number of conditions of within-S variables
  nWithinGroups    <- prod(vapply(ndatac[,withinvars, drop=FALSE], FUN=nlevels,
                                  FUN.VALUE=numeric(1)))
  correctionFactor <- sqrt( nWithinGroups / (nWithinGroups-1) )
  
  # Apply the correction factor
  ndatac$sd <- ndatac$sd * correctionFactor
  ndatac$se <- ndatac$se * correctionFactor
  ndatac$ci <- ndatac$ci * correctionFactor
  
  # Combine the un-normed means with the normed results
  merge(datac, ndatac)
}

# function that returns ICC 
reporticc <- function(output, places) {
  mainstat <- output$value
  upperci <- output$ubound
  lowerci <- output$lbound
  statistic <- paste("ICC=", round(mainstat,places), ", 95% CI [", round(lowerci, places), ", ", round(upperci, places), "]", sep = "")
  return(statistic)
}

# function that returns APA-formatted result from lme4/lmerTest table

# version 1 that reports ci, b, beta, se, p
report <- function(table, index, places, tails, flip) {
  if (tails == "1") {
    p.value <- round(table$p[index], 3)/2 # p values always rounded to 3 places
    howmanytails <- "one-tailed"
  } else {
    p.value <- round(table$p[index], 3) # p values always rounded to 3 places
    howmanytails <- "two-tailed"
  }
  if (p.value < .001) {
    p <- "<.001"
  } else {
    p <- paste("=", str_remove(p.value, "^0+"), sep = "") 
  }
  if (missing(flip)) {
    result <- paste("[", round(table$lower[index], places), ",", round(table$upper[index], places), "], ß=", round(table$beta[index], places), ", B=", round(table$est[index],places), ", SE=", round(table$se[index],places), ", p", p, ", ", howmanytails, sep = "")
  } else {
    result <- paste("[", -round(table$upper[index], places), ",", -round(table$lower[index], places), "], ß=", -round(table$beta[index], places), ", B=", -round(table$est[index],places), ", SE=", round(table$se[index],places), ", p", p, ", ", howmanytails, sep = "")
  }
  return(result)
}

# version 2, more condensed, that reports ci, beta, t(df), p
report2 <- function(table, index, places, tails, flip) {
  if (tails == "1") {
    p.value <- round(table$p[index], 3)/2 # p values always rounded to 3 places
    howmanytails <- "one-tailed"
  } else {
    p.value <- round(table$p[index], 3) # p values always rounded to 3 places
    howmanytails <- "two-tailed"
  }
  if (p.value < .001) {
    p <- "<.001"
  } else {
    p <- paste("=", str_remove(p.value, "^0+"), sep = "") 
  }
  if (missing(flip)) {
    result <- paste("[", round(table$lower[index], places), ",", round(table$upper[index], places), "], ß=", round(table$beta[index], places), ", t(", round(table$df[index],2), ")=", round(table$t[index], places), ", p", p, ", ", howmanytails, sep = "")
  } else {
    result <- paste("[", -round(table$upper[index], places), ",", -round(table$lower[index], places), "], ß=", -round(table$beta[index], places), ", t(", round(table$df[index],2), ")=", -round(table$t[index], places), ", p", p, ", ", howmanytails, sep = "")
  }
  return(result)
}
## get within-subjects CIs for plotting

# warning about Nan has to do with missing observations for control events
summary.avg <- summarySEwithin(data = risk.avg, measurevar = "look", betweenvars = c("exp"), withinvars = c("type", "phase"), idvar = "subj") %>%
  drop_na() %>%  
  mutate(cliff = type)
levels(summary.avg$cliff) <- c("deep", "deep", "shallow", "shallow")
levels(summary.avg$phase) <- c("control", "test")
# figure out how many looks are missing from the dataframe
nexclude <- wide %>%
   gather(type, look, control_1:test4) %>%
   filter(cost=="Risk") %>%
   mutate(missing = case_when(is.na(look) | str_detect(look, "NA") ~ 1)) %>%
   group_by(exp) %>%
   count(missing) %>%
  filter(!is.na(missing)) %>%
  rename(n_missing = n) %>%
  select(!missing)

experiments <- c("Exp.1", "Exp.2", "Exp.3")
totaltrials.Exp1 <- wide %>%
   gather(type, look, fam1:test4) %>% # no control trials
   filter(cost=="Risk") %>%
   filter(exp == "Exp.1") %>%
   group_by(exp) %>%
   tally() %>%
   rename(total = n)

totaltrials.Exp23 <- wide %>%
   gather(type, look, control_1:test4) %>%
   filter(cost=="Risk") %>%
   filter(exp == "Exp.2" | exp == "Exp.3") %>%
   group_by(exp) %>%
   tally() %>%
   rename(total = n)

ntrials <- full_join(nexclude, rbind(totaltrials.Exp1, totaltrials.Exp23)) %>% na.omit()

Reliability + Distribution Info

rel <- read.csv(file = "peril_reliability_deid.csv", header=TRUE)
exp1 <- rel %>% filter(experiment.paper=="Exp1")
exp2 <- rel %>% filter(experiment.paper=="Exp2")
exp3 <- rel %>% filter(experiment.paper=="Exp3")
exp4.study1 <- rel %>% filter(experiment.paper =="Exp4.Study1")
exp4.study2 <- rel %>% filter(experiment.paper =="Exp4.Study2")
exp4.study3 <- rel %>% filter(experiment.paper =="Exp4.Study3")

exp1rel <- icc(data.frame(exp1$secondary.look, exp1$orig.look),model="one", type="agreement")

exp2rel <- icc(data.frame(exp2$secondary.look, exp2$orig.look),model="one", type="agreement")

exp3rel <- icc(data.frame(exp3$secondary.look, exp3$orig.look),model="one", type="agreement")

exp4.study1.rel <- icc(data.frame(exp4.study1$secondary.look, exp4.study1$orig.look),model="one", type="agreement")

exp4.study2.rel <- icc(data.frame(exp4.study2$secondary.look, exp4.study2$orig.look),model="one", type="agreement")

exp4.study3.rel <- icc(data.frame(exp4.study3$secondary.look, exp4.study3$orig.look),model="one", type="agreement")
normal.ll <- fitdistr(na.omit(risk.avg$look), "normal")$loglik
lognormal.ll <- fitdistr(na.omit(risk.avg$look), "lognormal")$loglik

Figures

theme_set(theme_cowplot(font_size=20))

exp1.fig.data <- risk.avg %>% filter(task == "infer.value",
                                           phase == "testavg")
levels(exp1.fig.data$type) <- c(NA, "higher", "lower", NA)
exp1.fig.data$type <- relevel(exp1.fig.data$type, ref = "higher")
colors1 <- c(wes_palettes$Zissou1[3], wes_palettes$Zissou1[2])

risk1 <- ggplot(data = exp1.fig.data %>% filter(exp=="Exp.1"), aes(type, look, fill = type))+
  geom_boxplot()+
  scale_fill_manual(values=colors1)+
    geom_errorbar(data = summary.avg %>% filter(exp =="Exp.1"), colour="red", position = position_dodge(width = 5), width = 0, aes(ymin=look-ci, ymax=look+ci)) +
  stat_summary(fun.y = mean, alpha = 0.8, geom = "point", shape=21, size=3, position = "dodge", colour = "red", fill = "red") +
  ylab("Looking Time (s)") +
  xlab("Test event") +
  coord_cartesian(ylim = c(0, 65)) +
  geom_point(alpha = 0.1)+
  geom_line(alpha = 0.2, aes(group = subj))+
  theme(legend.position="none")+
  scale_x_discrete(labels = c("higher\nvalue", "lower\nvalue"))
  # annotate("text", colour="red", x=1.5, y=63, size=5, label=c("*ß=0.354", "ß=0.168")) +
  # facet_wrap(~exp, scales = "fixed", drop=TRUE)
  # theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))

risk1

Figure 2. Looking time towards test events in Experiment 2.

theme_set(theme_cowplot(font_size=20))

exp23.figure <- rbind(exp2.avg,exp3.avg) %>%
  filter(agegroup == "older") %>%
  mutate(cliff = case_when(type=="deep" | type =="higher" ~ "deep",
                           type=="shallow" | type =="lower" ~ "shallow"))
exp23.figure$cliff <- as.factor(exp23.figure$cliff)
exp23.figure$cliff <- relevel(exp23.figure$cliff, ref = "shallow")
exp23.figure$phase <- as.factor(exp23.figure$phase)

levels(exp23.figure$cliff)
## [1] "shallow" "deep"
levels(exp23.figure$phase) <- c("control", "test")
exp23.colors <- c(wes_palette("Royal2")[2], wes_palette("Royal2")[1])

exp2.figure <- ggplot(data = exp23.figure %>% filter(exp == "Exp.2"), aes(cliff, look, fill = cliff)) +
  geom_boxplot(aes(alpha=phase))+
  stat_summary(fun.y = mean, alpha = 0.8, geom = "point", shape=21, size=3, position = "dodge", colour = "red", fill = "red") +
  geom_errorbar(data = summary.avg %>% filter(exp == "Exp.2"), colour="red", position = position_dodge(width = 5), width = 0, aes(ymin=look-ci, ymax=look+ci)) +
  ylab("Looking Time (s)") +
  xlab("Cliff Depth") +
  coord_cartesian(ylim = c(0, 65)) +
  geom_point(alpha = 0.1)+
  geom_line(alpha = 0.2, aes(group = subj))+
  facet_wrap(~phase, nrow=1)+
  theme(legend.position="none")+
  scale_fill_manual(values = exp23.colors)+
  scale_alpha_discrete(range=c(0.4, 1))+
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))

exp3.figure <- ggplot(data = exp23.figure %>% filter(exp == "Exp.3"), aes(cliff, look, fill = cliff)) +
  geom_boxplot(aes(alpha=phase))+
  stat_summary(fun.y = mean, alpha = 0.8, geom = "point", shape=21, size=3, position = "dodge", colour = "red", fill = "red") +
  geom_errorbar(data = summary.avg %>% filter(exp == "Exp.3"), colour="red", position = position_dodge(width = 5), width = 0, aes(ymin=look-ci, ymax=look+ci)) +
  ylab("Looking Time (s)") +
  xlab("Cliff Depth") +
  coord_cartesian(ylim = c(0, 65)) +
  geom_point(alpha = 0.1)+
  geom_line(alpha = 0.2, aes(group = subj))+
  facet_wrap(~phase, nrow=1)+
  theme(legend.position="none")+
  scale_fill_manual(values = exp23.colors)+
  scale_alpha_discrete(range=c(0.4, 1))+
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))



exp2.figure + exp3.figure + plot_annotation(tag_levels = 'A')

Figure 4. Looking times from Experiments 2 (A, in-lab) and 3 (B, online) during the control events (lighter) and the test events (darker).

Experiment 1: Inferring value from risk

exp1.avg$type <- relevel(exp1.avg$type, ref = "higher")


exp1.0 <- lmer(loglook ~ 1 + (1|subj),
               data = exp1.avg)

exp1.1 <- lmer(loglook ~ type + (1|subj),
               data = exp1.avg)

# no influential observations
plot(influence(exp1.1, "subj"), which="cook",
     cutoff=4/32, sort=TRUE,
     xlab="Cook´s Distance",
     ylab="Subject ID")

exp1.1.table <- gen.m(exp1.1)
exp1.1.ci <- gen.ci(exp1.1)[3:4,]

exp1.1.beta <- lmer(scale(loglook) ~ type + (1|subj),
               data = exp1.avg )
exp1.1.betas <- gen.beta(exp1.1.beta)

exp1.results <- cbind(exp1.1.table, exp1.1.betas,exp1.1.ci)


# effect size
exp1.cohensd <- lme.dscore(exp1.1,
           data = exp1.avg %>% filter(subj != "S4_13"),
           type = "lme4") %>% select(d) %>% as.numeric()

Methods

Participants

Our final sample of participants included 32 thirteen-month-old infants (M=12.89 months, range=12.57-13.47, 17 female). Seven infants were excluded and replaced due to fussiness (3 infants) or inattentiveness during test trials (4 infants). Participants were recruited through a database of families who expressed interest in cognitive development research in the Boston area. Of the families in this database who chose to provide demographic information, 79.5% identified their children as White, 10.2% as Asian, 6.9% as Other, 2.5% as Black or African American, 0.4% as American Indian/Alaska Native, and 0.4% as Native Hawaiian/Pacific Islander; 90.3% as not Hispanic or Latino, 9.5% as Hispanic or Latino, and 0.2% as both. Most families in the database (90.4%) had at least one parent or legal guardian with a college diploma or higher. All data were collected at the Harvard Laboratory for Developmental Studies with procedures approved by the Committee on the Use of Human Subjects. We studied 13-month-old infants, rather than the 10-month-old infants tested in our past research [13]) because the younger infants lack experiences with walking and falling that may foster the development of these abilities. The sample size was chosen based on a simulation power analysis over the confirmatory analyses from 2 previous experiments with similar structure, conducted with 10-month-old infants: Experiments 1-2 from [13]), and we collected data until we attained our pre-specified N. The full pre-registration document, including full details about methods, sample size, hypotheses, and analysis plan, is available at https://osf.io/bfvdc/files/.

Data Coding and Analysis Strategy.

Infant looking times were coded online using XHAB (Pinto, 1995), and offline using Datavyu (Datavyu Team, 2014). All experimenters and coders were naive to the order of the test events and unable to see the video events (they relied on sound cues to start each trial). To check for exclusions and coding errors, all test trial data were re-coded in Datavyu and excluded if an infant looked away from a test event without ever having seen the agent jump, or if the trial ended too early or late (15 out of 320 total familiarization trials). We used these offline coded looking times for our final analyses. To assess the reliability of the data, (160 out of 320 trials) were re-coded in Datavyu by an additional researcher who was naive to test event order. Reliability was high, ICC=0.97, 95% CI [0.95, 0.98]. All decisions to include or exclude trials or participants from our analysis were made by researchers who did not know the order of events shown to that infant.

Infant looking times often are log-normally distributed, including in this dataset (log-likelihood of average looking times during test and control trials for Experiments 1-3 under normal distribution -2624.35, under lognormal distribution = -2456.26). Our pre-registered dependent measure therefore was the average looking time towards the higher- or lower-danger choice at test in log seconds. We report the values of unstandardized B coefficients and 95% confidence intervals in this unit, but our summary statistics and plots feature untransformed looking times for interpretability. We analyzed all looking times using mixed effects models (Bates et al., 2015) implemented in R (R Core Team, 2020). Analyses with repeated measures included a random intercept for participant identity; those conducted over multiple experiments included a random intercept for experiment. For every model, we checked for influential participants using Cook’s Distance (Nieuwenhuis et al., 2012) and excluded participants who exceeded the standard 4/n threshold, where n is the number of participants. The number of participants who met this criterion is listed in every model result; including or excluding them does not change the interpretation of any primary analysis (for results including all observations, see SOM). Data manipulation and plotting were conducted using tidyverse packages (Wickham et al., 2019). Cohen’s D derived from lme models were calculated using the EMAtools package (Kleiman, 2017). To enhance reproducibility, all results were written in R Markdown (Xie et al., 2018).

Results

Pre-registered results.

Infants looked longer when the agent chose the target achieved through the less dangerous action (Mlowervalue=24.6s, pooled standard error (SE)=1.14) than when the agent chose the target achieved through the more dangerous action (Mhighervalue=21.51s, SE=1.14 , 95% confidence interval (CI) over difference in log seconds [0.02,0.39], ß=0.34, t(31)=2.16, p=.039, two-tailed, Cohen’s d=0.79, no influential participants). As in the experiments of Liu et al. (2017) using closely similar methods, but presenting physically different actions on the two test trials, infants looked longer when this expected outcome did not occur.

Experiment 2: Minimizing Risk

exp2.test <- exp2.avg %>% filter(phase=="testavg") 

exp2.info <- info(exp2.avg)

exp2.0 <- lmer(loglook ~ 1 + (1|subj),
               data = exp2.test)

exp2.1 <- lmer(loglook ~ type + (1|subj),
               data = exp2.test)

# id influential observations
plot(influence(exp2.1, "subj"), which="cook",
     cutoff=4/30, sort=TRUE,
     xlab="Cook´s Distance",
     ylab="Subject ID")

# one influential observation

exp2.1.cooks <- lmer(loglook ~ type + (1|subj),
               data = exp2.test %>% filter(subj != "24-MR"))
exp2.1.table <- gen.m(exp2.1.cooks)
exp2.1.ci <- gen.ci(exp2.1.cooks)[3:4,]

exp2.1.beta <- lmer(scale(loglook) ~ type + (1|subj),
               data = exp2.test %>% filter(subj != "24-MR"))
exp2.1.betas <- gen.beta(exp2.1.beta)

exp2.results <- cbind(exp2.1.table, exp2.1.betas, exp2.1.ci)

# effect size
exp2.cohensd <- lme.dscore(exp2.1.cooks,
           data = exp2.test %>% filter(subj != "24-MR"),
           type = "lme4") %>% select(d) %>% as.numeric() *-1
exp2.control <- exp2.avg %>% filter(phase=="control") 

exp2.2 <- lmer(loglook ~ type + (1|subj),
               data = exp2.control)

# id influential observations
plot(influence(exp2.2, "subj"), which="cook",
     cutoff=4/30, sort=TRUE,
     xlab="Cook´s Distance",
     ylab="Subject ID")

# two influential observation

exp2.cooks <- lmer(loglook ~ type + (1|subj),
               data = exp2.control %>% filter(subj != "59-MR" & subj != "54-MR"))

exp2.table <- gen.m(exp2.cooks)
exp2.ci <- gen.ci(exp2.cooks)[3:4,]

exp2.beta <- lmer(scale(loglook) ~ type + (1|subj),
               data = exp2.control %>% filter(subj != "59-MR" & subj != "54-MR"))
exp2.betas <- gen.beta(exp2.beta)

exp2.results2 <- cbind(exp2.table, exp2.betas, exp2.ci)

# effect size
exp2.control.cohensd <- lme.dscore(exp2.cooks,
           data = exp2.control %>% filter(subj != "59-MR" & subj != "54-MR"),
           type = "lme4") %>% select(d) %>% as.numeric() *-1
exp2.control.test <- exp2.avg %>%
  mutate(cliff = case_when(type=="deep" | type =="higher" ~ "deep",
                           type=="shallow" | type =="lower" ~ "shallow"))

  
exp2.control.test$type <- as.factor(exp2.control.test$type)

exp2.3 <- lmer(loglook ~ cliff * phase + (1|subj),
                     data = exp2.control.test)

plot(influence(exp2.3, "subj"), which="cook",
     cutoff=4/30, sort=TRUE,
     xlab="Cook´s Distance",
     ylab="Subject ID")

# no influential observations

exp2.3.table <- gen.m(exp2.3)
exp2.3.ci <- gen.ci(exp2.3)[3:6,]

exp2.3.beta <- lmer(scale(loglook) ~ cliff * phase + (1|subj),
                     data = exp2.control.test)
exp2.3.betas <- gen.beta(exp2.3.beta)

exp2.results3 <- cbind(exp2.3.table,exp2.3.betas,exp2.3.ci)

# effect size
exp2.interaction.cohensd <- lme.dscore(exp2.3,
           data = exp2.control.test,
           type = "lme4") %>% slice(3) %>% select(d) %>% as.numeric() *-1

This study was originally pre-registered with a sample including both 10-month-old and 13-month-old infants. Because our investigation with 10-month-old infants is still ongoing, we deviate from our pre-registration by reporting only results from the older age group. Data from both age groups are open access at https://osf.io/kz7br/.

Methods

Participants

Our final sample of participants included 30 thirteen-month-old infants (M=12.89 months, range=12.53-13.5, 12 female). We chose this sample size using a simulation power analysis over the confirmatory analysis of data from a pilot study, as well as estimates of effect sizes of studies with similar displays and design (S. Liu et al., 2017; S. Liu & Spelke, 2017). Our pre-registration document is available at https://osf.io/efc3g/. We collected data until we attained our pre-specified N. Infants were excluded and replaced in the final sample due to fussiness that prevented study completion (3 infants), inattentiveness during test trials (2 infants), or interference from caregivers (2 infants).

Data Coding and Analysis

The data coding and analysis strategies were the same as in Experiment 1. Twenty-five out of 360 total familiarization, control, and test trials were excluded from the analysis based on inattentiveness or coding error. Half the test trials from the experiment (60/120 trials) were re-coded in Datavyu by an additional researcher who was naive to test event order. Reliability was high, ICC=1, 95% CI [1, 1].

Results

Infants looked longer when the agent, at test, chose to cross the deeper over the shallower trench (Mdeep=26.5s, SE=1.61; Mshallow=21.64s, SE=1.95; [0.03,0.43], ß=0.36, t(28)=2.33, p=.0135, one-tailed, d=0.88, excluding one influential participant).

In contrast, when infants’ attention was drawn to each trench by an attention-getting star that appeared in the path of the agent’s subsequent actions, infants looked longer at events near the shallow trench (Mdeep=12.73s, SE=1.11; Mshallow=16.02s, SE=2; [-0.31,-0.08], ß=-0.34, t(25.1)=-3.24, p=.003, two-tailed, d=-1.29,excluding 2 influential participants). Looking preferences between the control and test events differed significantly ([0.11,0.88], ß=0.75, t(84.74)=2.52, p=.013, two-tailed, d=0.55, no influential observations). See Figure 4A.

Experiment 3: Minimizing risk replication (no shattering)

exp3.control.test <- exp3.avg %>%
  mutate(cliff = case_when(type=="deep" | type =="higher" ~ "deep",
                           type=="shallow" | type =="lower" ~ "shallow"))

exp3.avg.test <- exp3.avg %>% filter(phase=="testavg") 

exp3.info <- info(exp3.avg)

exp3.0 <- lmer(loglook ~ 1 + (1|subj),
               data = exp3.avg.test)

exp3.1 <- lmer(loglook ~ type + (1|subj),
               data = exp3.avg.test)

# id influential observations
plot(influence(exp3.1, "subj"), which="cook",
     cutoff=4/30, sort=TRUE,
     xlab="Cook´s Distance",
     ylab="Subject ID")

# no influential observations

exp3.1.table <- gen.m(exp3.1)
exp3.1.ci <- gen.ci(exp3.1)[3:4,]

exp3.1.beta <- lmer(scale(loglook) ~ type + (1|subj),
               data = exp3.avg.test)
exp3.1.betas <- gen.beta(exp3.1.beta)

exp3.results <- cbind(exp3.1.table, exp3.1.betas, exp3.1.ci)

# effect size
exp3.cohensd <- lme.dscore(exp3.1,
           data = exp3.avg.test,
           type = "lme4") %>% select(d) %>% as.numeric() * 1
exp3.control <- exp3.control.test %>% filter(phase=="control") 

exp3.2 <- lmer(loglook ~ type + (1|subj),
               data = exp3.control)

# id influential observations
plot(influence(exp3.2, "subj"), which="cook",
     cutoff=4/42, sort=TRUE,
     xlab="Cook´s Distance",
     ylab="Subject ID")

# two influential observation

exp3.2.cooks <- lmer(loglook ~ type + (1|subj),
               data = exp3.control %>% filter(subj != "26" & subj != "28"))

exp3.2.table <- gen.m(exp3.2.cooks)
exp3.2.ci <- gen.ci(exp3.2.cooks)[3:4,]

exp3.2.beta <- lmer(scale(loglook) ~ type + (1|subj),
               data = exp3.control %>% filter(subj != "26" & subj != "28"))
exp3.2.betas <- gen.beta(exp3.2.cooks)

exp3.results2 <- cbind(exp3.2.table, exp3.2.betas, exp3.2.ci)

# effect size
exp3.control.cohensd <- lme.dscore(exp3.2.cooks,
           data = exp3.control %>% filter(subj != "26" & subj != "28"),
           type = "lme4") %>% select(d) %>% as.numeric() *-1
exp3.control.test$type <- as.factor(exp3.control.test$type)

exp3.3 <- lmer(loglook ~ cliff * phase + (1|subj),
                     data = exp3.control.test)

plot(influence(exp3.3, "subj"), which="cook",
     cutoff=4/30, sort=TRUE,
     xlab="Cook´s Distance",
     ylab="Subject ID")

# 1 influential observation

exp3.3.cooks <- lmer(loglook ~ cliff * phase + (1|subj),
                     data = exp3.control.test %>% filter(subj != "28"))

exp3.3.table <- gen.m(exp3.3.cooks)
exp3.3.ci <- gen.ci(exp3.3.cooks)[3:6,]

exp3.3.beta <- lmer(scale(loglook) ~ cliff * phase + (1|subj),
                     data = exp3.control.test %>% filter(subj != "28"))
exp3.3.betas <- gen.beta(exp3.3.beta)

exp3.results3 <- cbind(exp3.3.table,exp3.3.betas,exp3.3.ci)

# effect size
exp3.interaction.cohensd <- lme.dscore(exp3.3.cooks,
           data = exp3.control.test %>% filter(subj != "28"),
           type = "lme4") %>% slice(3) %>% select(d) %>% as.numeric() * -1
# pairwise0 <- lsmeans(exp3.3.cooks, list(pairwise~cliff|phase)) 
# 
# pairwise.beta <- lsmeans(exp3.3.beta, list(pairwise~cliff|phase)) 
# 
# pairwise.beta.value <- pairwise.beta[[2]] %>% as.data.frame() %>% select(contrast, phase, estimate) %>%
#   rename(beta = estimate)
# 
# pairwise.CI <- confint(pairwise0[[2]]) %>% as.data.frame()
# 
# pairwise.t.p <- pairwise0[[2]] %>% as.data.frame()
# 
# within.exp3.almost <- full_join(pairwise.CI, pairwise.t.p) %>%
#   rename(est = estimate,
#          se = SE,
#          lower = lower.CL,
#          upper = upper.CL,
#          t = t.ratio,
#          p = p.value)
# 
# within.exp3 <- full_join(pairwise.beta.value, within.exp3.almost)
exp3.devices<- wide %>%
  filter(exp == "Exp.3") %>%
  tabyl(device)

exp3.highchair <- wide %>%
  filter(exp == "Exp.3") %>%
  tabyl(highchair)

exp3.qualratings <- wide %>%
  filter(exp == "Exp.3") %>%
  summarise(vquality = mean(video_quality, na.rm=TRUE),
            vquality.sd = sd(video_quality, na.rm=TRUE),
            aquality = mean(audio_quality, na.rm=TRUE),
            aquality.sd = sd(audio_quality, na.rm=TRUE))

Methods

Participants

Our final sample included 42 twelve- to fifteen-month-old infants (M=13.95 months, range=12.29-15.67, 24 female): a widened age range that enabled more rapid testing of participants, who were recruited both from our lab database, also through a cross-institution platform for recruitment for developmental cognitive science (https://childrenhelpingscience.com/). Our preregistered target sample size of 40 was determined based on a simulation power analysis over infants’ looking preferences towards the test events from Experiment 2; our stopping rule was to stop recruiting as soon as we reached our target N, but to finish collecting data if we over-recruited. Thus, our final sample was N=42. A further 6 infants were excluded from the study (3 due to technical issues, 2 due to inattentiveness and 1 due to interference from the caregiver). Our pre-registration document is available at https://osf.io/96qsf/.

Procedure

Whereas Experiments 1 and 2 were conducted in a quiet, dark room in a lab setting, Experiment 3 was conducted over Zoom video conferencing, in infants’ homes, due to the COVID-19 pandemic, following procedures approved by the Committee on the Use of Human Subjects at Harvard University. We used materials developed by the Stanford Social Learning Lab (Social Learning Lab, 2020) to introduce caregivers to the online testing setup and to ask for verbal consent. Caregivers also provided written consent prior to the study session. Infants sat in a high chair (25 out of 42 participants) or their caregivers’ laps (17/42), depending on caregiver preferences, and watched the displays on a tablet (8/42) or a laptop computer (34/42). We asked caregivers, both before and during the study, to minimize distractions (pets, people walking by, and distracting objects) during the study session.

Before the experiment, infants saw a calibration video where their attention was drawn to the four corners of the screen, as well as the center of the screen. To maximize the quality of the events seen by infants, we shared our stimuli with caregivers through YouTube playlists, controlled the caregiver’s screen using Zoom’s remote control feature, and coded infants’ looking times during the study using jHab (Casstevens, 2007). Caregivers rated the quality of the audio and video on a 5-point Likert scale (1 = very poor; 5 = very good), giving high ratings, on average, for both (video: M=4.88, SD=0.33; audio: M=4.85, SD=0.36). After the session, we double checked for trial exclusions and generated the final data from the recording of the session video using Datavyu (Datavyu Team, 2014). As before, experimenters only had access to the video feed of infants’ faces (and not the displays) during the experiment, and therefore were unaware of the order of test events. To allow caregivers to attend to safety issues at home, we did not ask them to close their eyes, and instead instructed them to refrain from directing their infants’ attention toward or away from the screen. Our full online testing protocol is described in the SOM.

Data Coding and Analysis

The data coding and analysis strategy was identical to Experiment 2. Fifty-three out of NA total trials (including familiarization, test, and control trials) were excluded from analysis because of inattentiveness, distractions at home (e.g. pet noises, people walking by), technical issues and coding errors. The proportion of excluded trials (10.52%) was higher than what we observed in the lab in Experiment 2 (6.94%), due to distractions in the home environment, the smaller size of the screen displaying the videos at home, and the lower or more variable quality of the video feeds of the infants’ faces (which led to trial mis-timings). As in Experiments 1-2, 50% of the test trials were recoded by an additional naive coder (84 of 168 test trials). Interrater reliability was high, ICC=0.96, 95% CI [0.93, 0.97].

Results

Pre-registered results

We fully replicated the two key results from Experiment 2. Infants looked longer at test when the agent chose to jump the deeper trench (Mdeep=22.35s, SE=1.26; Mshallow=17.55s, SE=1; [0.09,0.41], ß=0.47, t(41)=3.06, p=.002, one-tailed, d=-0.96, no influential participants). Infants’ looking preferences between the control events and the test events significantly differed from each other ([0.13,0.75], ß=0.74, t(105.17)=2.76, p=.007, two-tailed, d=0.54, excluding 1 influential participant).

Exploratory results

During the control events, infants showed a numerical but non-significant preference for the event in which the inanimate object appeared over the shallower trench (Mdeep=12.09s, SE=1.27; Mshallow=13.9s, SE=1.48; [-0.42,0.07], ß=-0.17, t(64)=-1.39, p=.171, two-tailed, d=-0.35, excluding 2 influential participants). See Figure 4A.

Experiment 4, Studies 1-3 (10mo infants)

Study 1: Inferring value from risk (10-month-old infants)

exp1b.0 <- lmer(loglook ~ 1 + (1|subj),
               data = exp1b.avg)

exp1b.1 <- lmer(loglook ~ type + (1|subj),
               data = exp1b.avg)

# id influential observations
plot(influence(exp1b.1, "subj"), which="cook",
     cutoff=4/32, sort=TRUE,
     xlab="Cook´s Distance",
     ylab="Subject ID")

# one influential observation, exclude

exp1b.1.cooks <- lmer(loglook ~ type + (1|subj),
               data = filter(exp1b.avg, subj != "S5_25"))

exp1b.1.table <- gen.m(exp1b.1.cooks)
exp1b.1.ci <- gen.ci(exp1b.1.cooks)[3:4,]

exp1b.1.beta <- lmer(scale(loglook) ~ type + (1|subj),
               data = filter(exp1b.avg, subj != "S5_25"))
exp1b.1.betas <- gen.beta(exp1b.1.beta)

exp1b.results <- cbind(exp1b.1.table, exp1b.1.betas,exp1b.1.ci)

exp1b.cohensd <- lme.dscore(exp1b.1.cooks,
           data = filter(exp1b.avg, subj != "S5_25"),
           type = "lme4") %>% select(d) %>% as.numeric() 

Comparing older and younger infants

exp1.1013 <- long.avg %>% 
  filter(exp == "Exp.1" | exp == "Exp.1b") %>% 
  mutate(agegroup = as.factor(case_when(agem < 12 ~ "younger",
                            agem > 12 ~ "older")))

exp1.1013$type <- relevel(exp1.1013$type, ref = "higher")

exp1.1013.1 <- lmer(loglook ~ type * agegroup + (1|subj),
               data = exp1.1013)

# id influential observations
plot(influence(exp1.1013.1, "subj"), which="cook",
     cutoff=4/64, sort=TRUE,
     xlab="Cook´s Distance",
     ylab="Subject ID")

# one influential observation, exclude

exp1.1013.1.cooks <- lmer(loglook ~ type * agegroup + (1|subj),
               data = exp1.1013 %>% filter(subj != "S5_25"))

exp1.1013.1.table <- gen.m(exp1.1013.1.cooks)
exp1.1013.1.ci <- gen.ci(exp1.1013.1.cooks)[3:4,]

exp1.1013.1.beta <- lmer(scale(loglook) ~ type * agegroup  + (1|subj),
               data = exp1.1013 %>% filter(subj != "S5_25"))
exp1.1013.1.betas <- gen.beta(exp1.1013.1.beta)

exp1.1013.results <- cbind(exp1.1013.1.table, exp1.1013.1.betas,exp1.1013.1.ci)

exp1.1013.cohensd <- lme.dscore(exp1.1013.1.cooks,
           data = filter(exp1.1013, subj != "S5_25"),
           type = "lme4") %>% select(d) %>% slice(3) %>% as.numeric() 

Study 2: Minimizing danger

exp2b.test <- exp2b.avg %>% filter(phase=="testavg") 

exp2b.info <- info(exp2b.test)


exp2b.0 <- lmer(loglook ~ 1 + (1|subj),
               data = exp2b.test)

exp2b.1 <- lmer(loglook ~ type + (1|subj),
               data = exp2b.test)
summary(exp2b.1)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: loglook ~ type + (1 | subj)
##    Data: exp2b.test
## 
## REML criterion at convergence: 116
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.9659 -0.6026  0.0678  0.5726  1.7083 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subj     (Intercept) 0.244    0.494   
##  Residual             0.210    0.458   
## Number of obs: 60, groups:  subj, 30
## 
## Fixed effects:
##             Estimate Std. Error     df t value Pr(>|t|)    
## (Intercept)    2.997      0.123 45.005    24.4   <2e-16 ***
## typelower     -0.202      0.118 29.000    -1.7    0.099 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##           (Intr)
## typelower -0.481
# id influential observations
plot(influence(exp2b.1, "subj"), which="cook",
     cutoff=4/30, sort=TRUE,
     xlab="Cook´s Distance",
     ylab="Subject ID")

# no influential observations

exp2b.1.cooks <- lmer(loglook ~ type + (1|subj),
               data = exp2b.test %>% filter(subj != "03-MR"))
exp2b.1.table <- gen.m(exp2b.1.cooks)
exp2b.1.ci <- gen.ci(exp2b.1.cooks)[3:4,]

exp2b.1.beta <- lmer(scale(loglook) ~ type + (1|subj),
               data = exp2b.test %>% filter(subj != "03-MR"))
exp2b.1.betas <- gen.beta(exp2b.1.beta)

exp2b.1.results <- cbind(exp2b.1.table, exp2b.1.betas, exp2b.1.ci)

exp2b.test.cohensd <- lme.dscore(exp2b.1.cooks,
           data = exp2b.test %>% filter(subj != "03-MR"),
           type = "lme4") %>% select(d) %>% as.numeric() 
exp2b.control <- exp2b.avg %>% filter(phase=="control")

exp2b.2 <- lmer(loglook ~ type + (1|subj),
               data = exp2b.control)

# id influential observations
plot(influence(exp2b.2, "subj"), which="cook",
     cutoff=4/30, sort=TRUE,
     xlab="Cook´s Distance",
     ylab="Subject ID")

# one influential observation

exp2b.2.cooks <- lmer(loglook ~ type + (1|subj),
               data = exp2b.control %>% filter(subj != "21-MR"))

exp2b.2.table <- gen.m(exp2b.2.cooks)
exp2b.2.ci <- gen.ci(exp2b.2.cooks)[3:4,]

exp2b.2.beta <- lmer(scale(loglook) ~ type + (1|subj),
               data = exp2b.control %>% filter(subj != "21-MR"))
exp2b.2.betas <- gen.beta(exp2b.2.beta)

exp2b.results2 <- cbind(exp2b.2.table, exp2b.2.betas, exp2b.2.ci)

exp2b.control.cohensd <- lme.dscore(exp2b.2.cooks,
           data = exp2b.control %>% filter(subj != "21-MR"),
           type = "lme4") %>% select(d) %>% as.numeric() 
exp2b.control.test <- exp2b.avg %>%
  mutate(cliff = case_when(type=="deep" | type =="higher" ~ "deep",
                           type=="shallow" | type =="lower" ~ "shallow"))

# summary.mr <- summarySEwithin(data = exp2b.control.test, measurevar = "look", withinvars = c("cliff", "type", "phase"), idvar="subj")
  
exp2b.control.test$type <- as.factor(exp2b.control.test$type)

exp2b.3 <- lmer(loglook ~ cliff * phase + (1|subj),
                     data = exp2b.control.test)

plot(influence(exp2b.3, "subj"), which="cook",
     cutoff=4/30, sort=TRUE,
     xlab="Cook´s Distance",
     ylab="Subject ID")

exp2b.3.table <- gen.m(exp2b.3)
exp2b.3.ci <- gen.ci(exp2b.3)[3:6,]

exp2b.3.beta <- lmer(scale(loglook) ~ cliff * phase + (1|subj),
                     data = exp2b.control.test)
exp2b.3.betas <- gen.beta(exp2b.3.beta)

exp2b.results3 <- cbind(exp2b.3.table,exp2b.3.betas,exp2b.3.ci)

exp2b.interaction.cohensd <- lme.dscore(exp2b.3,
           data = exp2b.control.test,
           type = "lme4") %>% select(d) %>% slice(3) %>% as.numeric() 

Study 3: Minimizing danger (online, no shattering)

exp3b.devices<- wide %>%
  filter(exp == "Exp.3b") %>%
  tabyl(device)

exp3b.highchair <- wide %>%
  filter(exp == "Exp.3b") %>%
  tabyl(highchair)

exp3b.qualratings <- wide %>%
  filter(exp == "Exp.3b") %>%
  summarise(vquality = mean(video_quality, na.rm=TRUE),
            vquality.sd = sd(video_quality, na.rm=TRUE),
            aquality = mean(audio_quality, na.rm=TRUE),
            aquality.sd = sd(audio_quality, na.rm=TRUE))
exp3b.test <- exp3b.avg %>% filter(phase == "testavg")
exp3b.avg$type <- relevel(exp3b.avg$type, ref = "lower")


exp3b.1 <- lmer(data = exp3b.test, 
     formula = loglook ~ type + (1|subj))
summary(exp3b.1)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: loglook ~ type + (1 | subj)
##    Data: exp3b.test
## 
## REML criterion at convergence: 136
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.8600 -0.6556 -0.0803  0.7117  2.2508 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subj     (Intercept) 0.0594   0.244   
##  Residual             0.2513   0.501   
## Number of obs: 80, groups:  subj, 40
## 
## Fixed effects:
##             Estimate Std. Error      df t value Pr(>|t|)    
## (Intercept)   2.6459     0.0881 75.2501   30.02   <2e-16 ***
## typelower     0.0845     0.1121 39.0000    0.75     0.46    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##           (Intr)
## typelower -0.636
# 1 influential subject found
plot(influence(exp3b.1, "subj"), which="cook",
     cutoff=4/40, sort=TRUE,
     xlab="Cook´s Distance",
     ylab="Subject ID")

exp3b.1.cooks <- lmer(data = exp3b.test %>% filter(subj != "10m_33"), 
     formula = loglook ~ type + (1|subj))
summary(exp3b.1.cooks)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: loglook ~ type + (1 | subj)
##    Data: exp3b.test %>% filter(subj != "10m_33")
## 
## REML criterion at convergence: 126
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.0703 -0.6539 -0.0543  0.7160  1.7391 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subj     (Intercept) 0.0868   0.295   
##  Residual             0.2041   0.452   
## Number of obs: 78, groups:  subj, 39
## 
## Fixed effects:
##             Estimate Std. Error      df t value Pr(>|t|)    
## (Intercept)   2.6154     0.0864 69.7853   30.28   <2e-16 ***
## typelower     0.1357     0.1023 38.0000    1.33     0.19    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##           (Intr)
## typelower -0.592
exp3b.1.table <- gen.m(exp3b.1.cooks)
exp3b.1.ci <- gen.ci(exp3b.1.cooks)[3:4,]

exp3b.1.beta <- lmer(scale(loglook) ~type + (1|subj),
               data = exp3b.test %>% filter(subj != "10m_33"))
exp3b.1.betas <- gen.beta(exp3b.1.beta)

exp3b.1.results <- cbind(exp3b.1.table, exp3b.1.betas, exp3b.1.ci)

exp3b.test.cohensd <- lme.dscore(exp3b.1.cooks,
           data = exp3b.test %>% filter(subj != "10m_33"),
           type = "lme4") %>% select(d)  %>% as.numeric() 
exp3b.control.test <- exp3b.avg %>%
  mutate(cliff = case_when(type=="deep" | type =="higher" ~ "deep",
                           type=="shallow" | type =="lower" ~ "shallow"))
exp3b.control.test$cliff <- as.factor(exp3b.control.test$cliff)

exp3b.2 <- lmer(data = exp3b.control.test, 
     formula = loglook ~ cliff * phase + (1|subj))
summary(exp3b.2)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: loglook ~ cliff * phase + (1 | subj)
##    Data: exp3b.control.test
## 
## REML criterion at convergence: 263
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.8666 -0.7086 -0.0829  0.6966  2.3647 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subj     (Intercept) 0.049    0.221   
##  Residual             0.267    0.517   
## Number of obs: 154, groups:  subj, 40
## 
## Fixed effects:
##                           Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)                 2.3423     0.0922 142.0665   25.39   <2e-16 ***
## cliffshallow                0.0377     0.1203 112.0503    0.31    0.754    
## phasetestavg                0.3036     0.1181 112.8277    2.57    0.011 *  
## cliffshallow:phasetestavg   0.0468     0.1668 111.5145    0.28    0.780    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) clffsh phstst
## cliffshallw -0.652              
## phasetestvg -0.668  0.509       
## clffshllw:p  0.470 -0.721 -0.706
# no influential subjects detected
plot(influence(exp3b.2, "subj"), which="cook",
     cutoff=4/40, sort=TRUE,
     xlab="Cook´s Distance",
     ylab="Subject ID")

exp3b.2.table <- gen.m(exp3b.2)
exp3b.2.ci <- gen.ci(exp3b.2)[3:6,]

exp3b.2.beta <- lmer(scale(loglook) ~cliff * phase + (1|subj),
               data = exp3b.control.test)
exp3b.2.betas <- gen.beta(exp3b.2.beta)

exp3b.2.results <- cbind(exp3b.2.table, exp3b.2.betas, exp3b.2.ci)

exp3b.interaction.cohensd <- lme.dscore(exp3b.2,
           data = exp3b.control.test,
           type = "lme4") %>% select(d) %>% slice(3) %>% as.numeric() 
exp3b.control <- exp3b.avg %>%
  filter(phase=="control") %>%
  mutate(cliff = type)

exp3b.3 <- lmer(data = exp3b.control, 
     formula = loglook ~ cliff + (1|subj))
summary(exp3b.3)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: loglook ~ cliff + (1 | subj)
##    Data: exp3b.control
## 
## REML criterion at convergence: 129
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.8134 -0.6761 -0.0894  0.7408  2.0842 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subj     (Intercept) 0.0495   0.223   
##  Residual             0.2718   0.521   
## Number of obs: 74, groups:  subj, 38
## 
## Fixed effects:
##              Estimate Std. Error      df t value Pr(>|t|)    
## (Intercept)    2.3493     0.0932 70.5281   25.22   <2e-16 ***
## cliffshallow   0.0282     0.1215 36.1603    0.23     0.82    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## cliffshallw -0.652
plot(influence(exp3b.3, "subj"), which="cook",
     cutoff=4/40, sort=TRUE,
     xlab="Cook´s Distance",
     ylab="Subject ID")

exp3b.3.table <- gen.m(exp3b.3)
exp3b.3.ci <- gen.ci(exp3b.3)[3:4,]

exp3b.3.beta <- lmer(data = exp3b.control,
     formula = scale(loglook) ~ cliff + (1|subj))
exp3b.3.betas <- gen.beta(exp3b.3.beta)

exp3b.3.results <- cbind(exp3b.3.table, exp3b.3.betas, exp3b.3.ci)

exp3b.control.cohensd <- lme.dscore(exp3b.3,
           data = exp3b.control.test,
           type = "lme4") %>% 
  select(d) %>% as.numeric() 

Comparing Studies 2-3 and Experiments 2-3

exp23.1013.avg <- rbind(exp3.avg, exp2.avg, exp2b.avg, exp3b.avg) %>%
  mutate(cliff = case_when(type=="deep" | type =="higher" ~ "deep",
                           type=="shallow" | type =="lower" ~ "shallow"))

pooling.exp23 <- lmer(loglook ~ cliff * phase * agegroup + (1|subj) + (1|exp), data = exp23.1013.avg)

summary(pooling.exp23)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: loglook ~ cliff * phase * agegroup + (1 | subj) + (1 | exp)
##    Data: exp23.1013.avg
## 
## REML criterion at convergence: 973
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
## -3.697 -0.642 -0.032  0.623  2.376 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subj     (Intercept) 0.06820  0.2612  
##  exp      (Intercept) 0.00429  0.0655  
##  Residual             0.28228  0.5313  
## Number of obs: 546, groups:  subj, 142; exp, 4
## 
## Fixed effects:
##                                            Estimate Std. Error        df
## (Intercept)                                 2.36691    0.08763   6.64020
## cliffshallow                                0.16374    0.09404 399.12203
## phasetestavg                                0.67389    0.09215 408.33650
## agegroupyounger                            -0.00772    0.12270   6.38565
## cliffshallow:phasetestavg                  -0.42765    0.12917 398.14107
## cliffshallow:agegroupyounger               -0.08539    0.13148 399.09339
## phasetestavg:agegroupyounger               -0.23143    0.12948 404.54420
## cliffshallow:phasetestavg:agegroupyounger   0.31119    0.18219 398.10690
##                                           t value Pr(>|t|)    
## (Intercept)                                 27.01  4.9e-08 ***
## cliffshallow                                 1.74    0.082 .  
## phasetestavg                                 7.31  1.4e-12 ***
## agegroupyounger                             -0.06    0.952    
## cliffshallow:phasetestavg                   -3.31    0.001 ** 
## cliffshallow:agegroupyounger                -0.65    0.516    
## phasetestavg:agegroupyounger                -1.79    0.075 .  
## cliffshallow:phasetestavg:agegroupyounger    1.71    0.088 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) clffsh phstst aggrpy clffshllw:p clffshllw:g phsts:
## cliffshallw -0.546                                                    
## phasetestvg -0.565  0.519                                             
## agegropyngr -0.714  0.390  0.404                                      
## clffshllw:p  0.398 -0.728 -0.707 -0.284                               
## clffshllw:g  0.391 -0.715 -0.371 -0.541  0.521                        
## phststvg:gg  0.402 -0.370 -0.712 -0.554  0.503       0.512            
## clffshllw:: -0.282  0.516  0.502  0.390 -0.709      -0.722      -0.707
plot(allEffects(pooling.exp23))

lsmeans(pooling.exp23, pairwise ~ cliff * phase * agegroup)
## $lsmeans
##  cliff   phase   agegroup lsmean    SE  df lower.CL upper.CL
##  deep    control older      2.37 0.088 6.6     2.16      2.6
##  shallow control older      2.53 0.087 6.4     2.32      2.7
##  deep    testavg older      3.04 0.084 5.6     2.83      3.3
##  shallow testavg older      2.78 0.084 5.6     2.57      3.0
##  deep    control younger    2.36 0.086 6.1     2.15      2.6
##  shallow control younger    2.44 0.086 6.1     2.23      2.6
##  deep    testavg younger    2.80 0.085 5.8     2.59      3.0
##  shallow testavg younger    2.76 0.085 5.8     2.55      3.0
## 
## Degrees-of-freedom method: kenward-roger 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast                                          estimate    SE  df t.ratio
##  deep control older - shallow control older           -0.16 0.094 401 -1.700 
##  deep control older - deep testavg older              -0.67 0.092 410 -7.300 
##  deep control older - shallow testavg older           -0.41 0.092 410 -4.400 
##  deep control older - deep control younger             0.01 0.123   6  0.100 
##  deep control older - shallow control younger         -0.07 0.123   6 -0.600 
##  deep control older - deep testavg younger            -0.43 0.122   6 -3.600 
##  deep control older - shallow testavg younger         -0.40 0.122   6 -3.200 
##  shallow control older - deep testavg older           -0.51 0.091 408 -5.600 
##  shallow control older - shallow testavg older        -0.25 0.091 408 -2.700 
##  shallow control older - deep control younger          0.17 0.122   6  1.400 
##  shallow control older - shallow control younger       0.09 0.122   6  0.800 
##  shallow control older - deep testavg younger         -0.27 0.121   6 -2.200 
##  shallow control older - shallow testavg younger      -0.23 0.121   6 -1.900 
##  deep testavg older - shallow testavg older            0.26 0.089 399  3.000 
##  deep testavg older - deep control younger             0.68 0.120   6  5.700 
##  deep testavg older - shallow control younger          0.60 0.120   6  5.000 
##  deep testavg older - deep testavg younger             0.24 0.120   6  2.000 
##  deep testavg older - shallow testavg younger          0.28 0.120   6  2.300 
##  shallow testavg older - deep control younger          0.42 0.120   6  3.500 
##  shallow testavg older - shallow control younger       0.34 0.120   6  2.800 
##  shallow testavg older - deep testavg younger         -0.02 0.120   6 -0.200 
##  shallow testavg older - shallow testavg younger       0.01 0.120   6  0.100 
##  deep control younger - shallow control younger       -0.08 0.092 401 -0.900 
##  deep control younger - deep testavg younger          -0.44 0.091 403 -4.900 
##  deep control younger - shallow testavg younger       -0.40 0.091 403 -4.400 
##  shallow control younger - deep testavg younger       -0.36 0.091 403 -4.000 
##  shallow control younger - shallow testavg younger    -0.33 0.091 403 -3.600 
##  deep testavg younger - shallow testavg younger        0.04 0.090 399  0.400 
##  p.value
##  0.6600 
##  <.0001 
##  <.0001 
##  1.0000 
##  1.0000 
##  0.1100 
##  0.1500 
##  <.0001 
##  0.1300 
##  0.8300 
##  0.9900 
##  0.4400 
##  0.5800 
##  0.0600 
##  0.0100 
##  0.0300 
##  0.5400 
##  0.4100 
##  0.1200 
##  0.2400 
##  1.0000 
##  1.0000 
##  0.9900 
##  <.0001 
##  <.0001 
##  <.0001 
##  0.0100 
##  1.0000 
## 
## Degrees-of-freedom method: kenward-roger 
## P value adjustment: tukey method for comparing a family of 8 estimates
plot(influence(pooling.exp23, "subj"), which="cook",
     cutoff=4/142, sort=TRUE,
     xlab="Cook´s Distance",
     ylab="Subject ID")

pooling.exp23.cooks <- lmer(loglook ~ cliff * phase * agegroup + (1|subj) + (1|exp), data = exp23.1013.avg %>% filter(subj != "28"))
summary(pooling.exp23.cooks)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: loglook ~ cliff * phase * agegroup + (1 | subj) + (1 | exp)
##    Data: exp23.1013.avg %>% filter(subj != "28")
## 
## REML criterion at convergence: 952
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.9448 -0.6493 -0.0278  0.6330  2.4232 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subj     (Intercept) 0.07202  0.2684  
##  exp      (Intercept) 0.00405  0.0636  
##  Residual             0.27168  0.5212  
## Number of obs: 542, groups:  subj, 141; exp, 4
## 
## Fixed effects:
##                                            Estimate Std. Error        df
## (Intercept)                                 2.35593    0.08675   6.69366
## cliffshallow                                0.20654    0.09299 396.05008
## phasetestavg                                0.67616    0.09112 405.16215
## agegroupyounger                             0.00271    0.12115   6.36409
## cliffshallow:phasetestavg                  -0.46520    0.12767 395.09467
## cliffshallow:agegroupyounger               -0.12780    0.12952 396.00722
## phasetestavg:agegroupyounger               -0.23331    0.12755 401.42519
## cliffshallow:phasetestavg:agegroupyounger   0.34835    0.17942 395.05325
##                                           t value Pr(>|t|)    
## (Intercept)                                 27.16  4.2e-08 ***
## cliffshallow                                 2.22   0.0269 *  
## phasetestavg                                 7.42  6.9e-13 ***
## agegroupyounger                              0.02   0.9829    
## cliffshallow:phasetestavg                   -3.64   0.0003 ***
## cliffshallow:agegroupyounger                -0.99   0.3244    
## phasetestavg:agegroupyounger                -1.83   0.0681 .  
## cliffshallow:phasetestavg:agegroupyounger    1.94   0.0529 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) clffsh phstst aggrpy clffshllw:p clffshllw:g phsts:
## cliffshallw -0.546                                                    
## phasetestvg -0.565  0.519                                             
## agegropyngr -0.716  0.391  0.405                                      
## clffshllw:p  0.398 -0.728 -0.707 -0.285                               
## clffshllw:g  0.392 -0.718 -0.373 -0.540  0.523                        
## phststvg:gg  0.404 -0.371 -0.714 -0.553  0.505       0.512            
## clffshllw:: -0.283  0.518  0.503  0.390 -0.712      -0.722      -0.707
pooling.exp23.table <- gen.m(pooling.exp23.cooks)
pooling.exp23.ci <- gen.ci(pooling.exp23.cooks)[3:10,]

pooling.exp23.betas <- gen.beta(pooling.exp23.cooks)

pooling.exp23.results <- cbind(pooling.exp23.table, pooling.exp23.betas, pooling.exp23.ci)

pooling.exp23.cohensd <- lme.dscore(pooling.exp23.cooks,
           data = exp23.1013.avg %>% filter(subj != "28"),
           type = "lme4") %>% slice(7) %>%
  select(d) %>% as.numeric()
exp23.1013.test <- exp23.1013.avg %>% filter(phase=="testavg")
exp23.1013.control <- exp23.1013.avg %>% filter(phase=="control")

pooling.exp23.test <- lmer(loglook ~ cliff * agegroup + (1|subj) + (1|exp), data = exp23.1013.test)

plot(influence(pooling.exp23.test, "subj"), which="cook",
     cutoff=4/142, sort=TRUE,
     xlab="Cook´s Distance",
     ylab="Subject ID")

pooling.exp23.test.cooks <- lmer(loglook ~ cliff * agegroup + (1|subj) + (1|exp), data = exp23.1013.test %>% filter(subj != "10m_33"))

pooling.exp23.test.table <- gen.m(pooling.exp23.test.cooks)
pooling.exp23.test.ci <- gen.ci(pooling.exp23.test.cooks)[4:7,]
pooling.exp23.test.betas <- gen.beta(pooling.exp23.test.cooks)

pooling.exp23.test.results <- cbind(pooling.exp23.test.table, pooling.exp23.test.betas, pooling.exp23.test.ci)

pooling.exp23.test.cohensd <- lme.dscore(pooling.exp23.test.cooks,
           data = exp23.1013.test %>% filter(subj != "10_33"),
           type = "lme4") %>% slice(3) %>%
  select(d) %>% as.numeric()

pooling.exp23.control <- lmer(loglook ~ cliff * agegroup + (1|subj) + (1|exp), data = exp23.1013.control)

plot(influence(pooling.exp23.control, "subj"), which="cook",
     cutoff=4/142, sort=TRUE,
     xlab="Cook´s Distance",
     ylab="Subject ID")

pooling.exp23.control.cooks <- lmer(loglook ~ cliff * agegroup + (1|subj) + (1|exp), data = exp23.1013.control %>% filter(subj != "28" & subj != "26"))

pooling.exp23.control.table <- gen.m(pooling.exp23.control.cooks)
pooling.exp23.control.ci <- gen.ci(pooling.exp23.control.cooks)[4:7,]
pooling.exp23.control.betas <- gen.beta(pooling.exp23.control.cooks)

pooling.exp23.control.results <- cbind(pooling.exp23.control.table, pooling.exp23.control.betas, pooling.exp23.control.ci)

pooling.exp23.control.cohensd <- lme.dscore(pooling.exp23.control.cooks,
           data = exp23.1013.control %>% filter(subj != "28" & subj != "26"),
           type = "lme4") %>% slice(3) %>%
  select(d) %>% as.numeric()

In Experiment 4, we investigated the developmental origins of the capacity to reason about danger by testing infants under one year of age, using the respective methods of Experiments 1-3. We will reference these samples as Experiment 4, Studies 1, 2, and 3. All 3 studies focused on 10-month-olds because of their previous success in reasoning about the physical costs of actions (e.g. in [redacted]).

Methods

Participants

Our final sample included a grand total of 102 10-month-old infants. Studies 1-2 were conducted in the lab, and our final sample included 32 infants in Study 1 (M=10.13 months, range=9.6-10.63, 15 female; an additional 6 infants tested and excluded from the final sample), and 30 infants in Study 2 (M=9.95 months, range=8.97-10.47, 17 female; an additional 2 infants tested and excluded). In Study 3, we collected an online sample of 40 (M=(M=10.24 months, range=9.53-11.06, 20 female, xxxx excluded). In the online sample, infants sat in a high chair (13 out of 40 participants) or their caregivers’ laps (27/40), depending on caregiver preferences, and watched the displays on a tablet (12/40) or a laptop computer (28/40). Caregivers gave high ratings for both the video quality (M=4.88, SD=0.33) and audio quality (M=4.86, SD=0.34). All three of these studies were pre-registered (Study 1: https://osf.io/uh8ns/; Study 2: https://osf.io/kx928/, Study 3: https://osf.io/48j9v/) Data reliability. As in Experiments 1-3, the reliability of the looking time data in Experiment 4 were high (Study 1: ICC=0.995, 95% CI [0.991, 0.997]; Study 2: ICC=0.999, 95% CI [0.998, 0.999]; Study 3:ICC=0.909, 95% CI [0.859, 0.942]).

Results

Inferring value from danger (Study 1)

When we tested 10-month-old infants using identical protocols as reported in Experiment 1, these younger infants did not show a statistically significant looking preference between the test events, (Mhighervalue=19.15s, Mlowervalue= 19.51, pooled SE=1.22, [-0.121,0.301], ß=0.168, B=0.09, SE=0.106, p=.202, one-tailed d=0.31, removing 1 influential participant). Comparing the data from Experiment 1 and Experiment 4, Study 1, 10- and 13-month-old infants did not significantly differ in their looking preferences in this task, [0.009,0.4], ß=-0.202, B=-0.115, SE=0.142, p=.422, two-tailed, d=-0.21, no influential participants.

Avoiding danger (Studies 2-3)

When we tested 10-month-old infants in identical protocols as Experiment 2, ten-month-old looked longer at test when the agent chose the deeper the shallower trench (Mdeeper=24.97s, Mshallower= 20.31, pooled SE=1.51, [-0.472,-0.047], ß=-0.386, B=-0.26, SE=0.107, p=.011, one-tailed d=0.92, removing 1 influential participant). During control events, 10-month-old infants did not show a significant looking preference (Mdeeper=12.74s, Mshallower= 14.68, pooled SE=1.97, [-0.174,0.301], ß=0.109, B=0.064, SE=0.119, p=.598, two-tailed d=-0.2, excluding 1 influential participant). In contrast to the data from older infants, these two patterns of looking preference did not differ from each other, [-0.728,0.073], ß=-0.483, B=-0.327, SE=0.205, p=.115, two-tailed, d=0.05no influential participants.

Notably, we did not replicate the results of Study 2 when we ran an additional online sample of infants: In Study 3, 10-month-old infants did not show a looking preference during the test events (Mdeeper=16.03s, Mshallower= 18.22, pooled SE=1.26, [-0.067,0.339], ß=0.251, B=0.136, SE=0.102, p=.0965, one-tailed d=0.43, excluding 1 influential participant), or the control events (Mdeeper=12.74s, Mshallower= 12.3, pooled SE=1.24, [-0.213,0.269], ß=0.05, B=0.028, SE=0.121, p=.818, two-tailed d=-0.13, no influential participants), and their looking preferences did not differ across the two phases of the experiment, [-0.28,0.37], ß=0.08, B=0.05, SE=0.17, p=.78, two-tailed, d=0.05, no influential observations.

Pooling data across older and younger infants tested in Experiments 2-3, and Experiment 4, Studies 2-3, we found a marginal 3-way interaction between cliff depth (shallow vs deep), phase of experiment (control vs test), and age group (infants younger than 1y vs older than 1y), [-0.482,0.016], ß=0.348, B=0.348, SE=0.179, p=.053, two-tailed, d=0.2, 1 influential participant.

This interaction appeared to be driven by differences in younger and older infants’ responses to the test events: we found a significant interaction between age group (younger vs older than 1y) and cliff depth (shallow vs deep) for the test events, [0.053,0.453], ß=0.253, B=0.253, SE=0.103, p=.015, two-tailed, d=0.36, excluding one influential participant, but not the control events, [-0.362,0.119], ß=-0.121, B=-0.121, SE=0.123, p=.326, two-tailed, d=-0.18excluding two influential participants.

Thus, in a large and sufficiently powered sample (N=142), infants younger and infants older than 1 year of age differed in their pattern of looking responses to events where agents choose more vs less dangerous actions, but did not differ when their attention was simply drawn to the physical trenches where these actions occurred.

Supplemental Materials

# is a lognormal transformation justified given the distribution of looks?
fig.S1 <- ggplot(data = risk.avg, aes(look, fill=exp))
fig.S1 +
  geom_density(alpha = 0.5)+
  # geom_text(aes(experiment))+
  theme_cowplot(20)+
  # facet_wrap(~exp)+
  xlab("Looking Time (s)")

  # scale_fill_brewer(palette="Set2")

Figure S1. Density plot of looking times during test for Experiment 1 and from test events and control events for Experiments 2-3. Maximum-likelihood fitting revealed that the lognormal distribution (log likelihood=-2456.26) provides a better fit to these data than the normal distribution (log likelihood=-2624.35).

fam <- wide %>%
  filter(cost=="Risk")%>%
  gather(trial, look, fam1:test4) %>%
  mutate(trial_n = parse_number(trial)) %>%
  mutate(trial_type = str_extract(trial, "[a-z]+"))

fam$trial_type <- as.factor(fam$trial_type)
fam$trial_n <- as.factor(fam$trial_n)
fam$look <- as.numeric(as.character(fam$look))

famplot <- ggplot(data = fam, aes(trial_n, look, fill=trial_type))
famplot + geom_boxplot() +
  facet_wrap(~exp+trial_type, nrow=2)+
  xlab("Trial N")+
  stat_summary(fun.data =mean_cl_boot, geom="errorbar",width=0.1)+
    stat_summary(fun.y=mean,geom="point",shape=5)+
  ylab("Looking time (s)")+
  theme_cowplot(20)

Figure S2. Boxplots of looking times during familiarization and test across Experiments (total N=206). Error bars represent bootstrapped 95% confidence intervals around the mean.

Including influential observations (reviewer suggestion)

Below, we report the results from our pre-registered analyses including all observations, rather than excluding influential observations. We report the analysis for Exp 2 and 3 only because no influential observations were observed in the confirmatory analysis of Experiment 1.

exp2.everyone.test.table <- gen.m(exp2.1)

exp2.everyone.test.ci <- gen.ci(exp2.1)[3:4,]

exp2.everyone.test.beta <- lmer(scale(loglook)~ type + (1 | subj),
                                     data=exp2.test)

exp2.everyone.test.betas <- gen.beta(exp2.everyone.test.beta)

exp2.everyone.test.results <- cbind(exp2.everyone.test.betas,exp2.everyone.test.table,exp2.everyone.test.ci)

exp2.everyone.pre.table <- gen.m(exp2.2)
exp2.everyone.pre.ci <- gen.ci(exp2.2)[3:4,]

exp2.everyone.pre.beta <- lmer(scale(loglook) ~ type + (1 | subj),
                                     data=exp2.control)
exp2.everyone.pre.betas <- gen.beta(exp2.everyone.pre.beta)

exp2.everyone.pre.results <- cbind(exp2.everyone.pre.betas,exp2.everyone.pre.table,exp2.everyone.pre.ci)

exp2.everyone.prevstest.table <- gen.m(exp2.3)
exp2.everyone.prevstest.ci <- gen.ci(exp2.3)[3:6,]

exp2.everyone.prevstest.beta <- lmer(scale(loglook) ~ cliff * phase + (1|subj), data = exp2.control.test)

exp2.everyone.prevstest.betas <- gen.beta(exp2.everyone.prevstest.beta)

exp2.everyone.prevstest.results <- cbind(exp2.everyone.prevstest.betas,exp2.everyone.prevstest.table,exp2.everyone.prevstest.ci)
exp3.everyone.test.table <- gen.m(exp3.1)
exp3.everyone.test.ci <- gen.ci(exp3.1)[3:4,]

exp3.everyone.test.beta <- lmer(scale(loglook)~ type + (1 | subj),
                                     data=exp3.avg)
exp3.everyone.test.betas <- gen.beta(exp3.everyone.test.beta)

exp3.everyone.test.results <- cbind(exp3.everyone.test.betas,exp3.everyone.test.table,exp3.everyone.test.ci)

exp3.everyone.pre.table <- gen.m(exp3.2)
exp3.everyone.pre.ci <- gen.ci(exp3.2)[3:4,]

exp3.everyone.pre.beta <- lmer(scale(loglook) ~ type + (1 | subj),
                                     data=exp3.control)
exp3.everyone.pre.betas <- gen.beta(exp3.everyone.pre.beta)

exp3.everyone.pre.results <- cbind(exp3.everyone.pre.betas,exp3.everyone.pre.table,exp3.everyone.pre.ci)

exp3.everyone.prevstest.table <- gen.m(exp3.3)
exp3.everyone.prevstest.ci <- gen.ci(exp3.3)[3:6,]

exp3.everyone.prevstest.beta <- lmer(scale(loglook) ~ cliff * phase + (1|subj), data = exp3.control.test)

exp3.everyone.prevstest.betas <- gen.beta(exp3.everyone.prevstest.beta)

exp3.everyone.prevstest.results <- cbind(exp3.everyone.prevstest.betas,exp3.everyone.prevstest.table,exp3.everyone.prevstest.ci)

Experiment 2

Infants looked longer at test when the agent, at test, chose the deeper trench over the shallower trench ([0.07,0.51], ß=0.43, t(29)=2.59, p=.0075, one-tailed). During control events, 13-month-old infants preferred to look at the shallow trench ([-0.37,-0.02], ß=-0.34, t(27.3)=-2.2, p=.036, two-tailed). Their looking preferences significantly differed across the two phases of the experiment, [0.11,0.88], ß=0.75, t(84.74)=2.52, p=.013, two-tailed). These findings accord with those reported in the main text and support the interpretation that infants expected the agent to take the less dangerous action and therefore showed a greater looking preference for the test event than for the control event presenting events over the deeper trench.

Experiment 3

Infants looked longer at test when the agent chose to jump over the deeper trench ([0.09,0.41], ß=-1.05, t(41)=3.06, p=.002, one-tailed). During control events, infants did not show a looking preference for either event ([-0.41,0.17], ß=-0.2, t(68)=-0.82, p=.418, two-tailed). Their looking preferences significantly differed across the test and control trials ([0.04,0.7], ß=0.6, t(108.49)=2.18, p=.032, two-tailed). This finding fully replicates the two key findings from Experiment 2 and accords with the findings reported in the main text.

Order effects in Experiment 1 (reviewer-requested exploratory analysis)

exp1.order <- lmer(data = exp1.avg,
                   formula = loglook ~ first_fam * type + (1|subj))
exp1.order.table <- gen.m(exp1.order)
exp1.order.ci <- gen.ci(exp1.order)[3:6,]


exp1.order.betas <- lmer(data = exp1.avg,
                   formula = scale(loglook) ~ first_fam * type + (1|subj))
exp1.order.beta <- gen.beta(exp1.order.betas)

exp1.ordereffects <- cbind(exp1.order.table, exp1.order.beta, exp1.order.ci)

Infants’ looking preferences at test did not vary depending on which sequence of events (low to high danger vs high to low danger) they were randomly assigned to watch in the first familiarization trial ([-0.2,0.55], ß=0.29, t(30)=0.93, p=.362, two-tailed). All infants saw both trial orders for 3 familiarization trials each.

Looking time to each familiarization event in Experiment 1 (reviewer-requested exploratory analysis)

detach("package:dplyr", unload = TRUE)
library(dplyr)
exp1.fam <- read.csv("./exp1_fam_csvs/exp1_fam_looks.csv", header=TRUE)

exp1.fam <- exp1.fam %>%
  separate(videoclip, into = c("depth", "yesno"), remove=FALSE) %>%
  rename(subj= subjID)

exp1.fam$depth <- as.factor(exp1.fam$depth)
exp1.fam$yesno <- as.factor(exp1.fam$yesno)
exp1.fam$trial <- as.factor(exp1.fam$trial)
exp1.fam$subj <- as.factor(exp1.fam$subj)
exp1.fam$videoclip <- as.factor(exp1.fam$videoclip)

exp1.fam.glancedoff <- exp1.fam %>%
  mutate(glanced.off = case_when(proportion.on == 1.0 ~ 0,
                                proportion.on < 1.0 ~ 1))

exp1.fam.glancedoff.totalclips <- exp1.fam.glancedoff %>%
  select(subj, depth, videoclip, glanced.off) %>%
  group_by(subj, videoclip) %>%
  summarise(totalclips = n()) 

exp1.fam.glancedoff.freq <- exp1.fam.glancedoff %>%
  select(subj, depth, videoclip, glanced.off) %>%
  group_by(subj, videoclip) %>%
  tally(glanced.off)

exp1.fam.glanced.off.summary <- full_join(exp1.fam.glancedoff.totalclips, exp1.fam.glancedoff.freq) %>%
  mutate(prop.glancedoff = n/totalclips) %>%
  mutate(depth = case_when(videoclip == "deep_no" ~ "deep",
                           videoclip == "shallow_yes" ~ "shallow",
                           videoclip == "medium_no" ~ "medium",
                           videoclip == "medium_yes" ~ "medium"))

exp1.fam.glanced.off.summary$videoclip <- factor(exp1.fam.glanced.off.summary$videoclip, levels=c("shallow_yes", "medium_no", "medium_yes", "deep_no"))
figS3 <- exp1.fam.glanced.off.summary %>%
  ggplot(aes(videoclip, prop.glancedoff, fill=depth)) + 
  geom_boxplot() +
  geom_point(alpha=0.3) +
  geom_line(alpha = 0.2, aes(group = subj)) +
  ylab("Proportion of events including look away") +
  xlab("Event type") +
  # facet_wrap(~depth) + 
  stat_summary(fun.data =mean_cl_boot, geom="errorbar",width=0.2)+
    stat_summary(fun=mean,geom="point",shape=5, size=3)+ theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))

figS3

Figure S3. Proportion of events during which infants glanced away from the screen, relative to how many times infants saw each event. Data come from a random subset of infants in Experiment 1 (N=16 out of 32 total infants), with observations grouped by infant (points connected by grey lines). Error bars represent bootstrapped 95% confidence intervals around the mean. Infants look away from the screen with roughly equal probabilities across the 4 event types.

# compute 4 values per subject, total proportion to
# deep_no, medium_yes, medium_no, shallow_yes
exp1.fam.bymovie <- exp1.fam %>%
  group_by(subj,videoclip, depth, yesno) %>%
  mutate(proportion.on.total = mean(proportion.on)) %>%
  distinct(proportion.on.total)

exp1.fam.bymovie.wide <- exp1.fam.bymovie %>%
  pivot_wider(names_from = videoclip, values_from = proportion.on.total, id=subj)

exp1.avg.diff <- exp1.avg %>%
  filter(phase == "testavg") %>%
  pivot_wider(names_from = type, values_from = look, id=subj) %>%
  mutate(delta.look = lower-higher)

exp1.fam.glancedoff.wide <- exp1.fam.glanced.off.summary %>%
  pivot_wider(names_from = videoclip, values_from = prop.glancedoff, id=subj)

exp1.famtest <- full_join(exp1.fam.bymovie.wide, exp1.avg.diff, by=c("subj")) %>% 
  na.omit()
exp1.famtest.long <- exp1.famtest %>%
  gather(key = "movie_clip", value = "proportion_looking", shallow_yes:deep_no)

exp1.famtest.glanceoff <- full_join(exp1.fam.glancedoff.wide, exp1.avg.diff, by=c("subj")) %>% 
  na.omit()

exp1.famtest.glanceofflong <- exp1.famtest.glanceoff %>%
  gather(key = "movie_clip", value = "proportion_glanced_off", shallow_yes:deep_no)
theme_set(theme_cowplot(font_size=15))

figS4A <- 
exp1.famtest.long %>%
  ggplot(aes(proportion_looking, delta.look)) + 
  geom_point() +
  geom_smooth(method="lm") +
  # geom_line(alpha = 0.2, aes(group = subjID)) +
  xlab("Total proportion looking \n to movie clip") +
  ylab("Looking preference at test (s)\n
       <--- Longer looking to expected ---- Longer looking to unexpected --->") +
  facet_wrap(~movie_clip)

figS4A

Figure Sx [not in final SOM] Scatter plot of average proportion looking to each movie clip from familiarization and looking preferences at test.

cor.data <- exp1.famtest.glanceoff %>%
  select(shallow_yes:deep_no, delta.look) %>%
  rename(VOE_response = delta.look) %>%
  as.data.frame() 


corrplot(cor(cor.data[,-1]),
         method='circle',
         type='lower',
         addCoef.col ='black',
         diag=FALSE)

Figure S4. Correlation plot relating infants’ likelihood of looking away from each of the 4 familiarization events (proportion of events including a look away) to one another, and to infants’ violation of expectation response (unexpected - expected) at test. Values indicate Pearson’s correlations. Descriptively, the more infants looked away from the events, the smaller VOE response they showed at test.

fam.glance1 <- lmer(data = exp1.fam.glanced.off.summary,
               formula = prop.glancedoff ~ videoclip + (1|subj))
summary(fam.glance1)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: prop.glancedoff ~ videoclip + (1 | subj)
##    Data: exp1.fam.glanced.off.summary
## 
## REML criterion at convergence: 6.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.3654 -0.5786  0.0699  0.7238  1.4625 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subj     (Intercept) 0.0304   0.174   
##  Residual             0.0376   0.194   
## Number of obs: 64, groups:  subj, 16
## 
## Fixed effects:
##                     Estimate Std. Error      df t value Pr(>|t|)    
## (Intercept)           0.5552     0.0652 37.4836    8.51  2.7e-10 ***
## videoclipmedium_no   -0.0208     0.0685 45.0000   -0.30     0.76    
## videoclipmedium_yes  -0.0333     0.0685 45.0000   -0.49     0.63    
## videoclipdeep_no     -0.0958     0.0685 45.0000   -1.40     0.17    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) vdclpmdm_n vdclpmdm_y
## vidclpmdm_n -0.526                      
## vdclpmdm_ys -0.526  0.500               
## videclpdp_n -0.526  0.500      0.500
tab_model(fam.glance1, show.stat=TRUE,show.df=TRUE)
  prop glancedoff
Predictors Estimates CI Statistic p df
(Intercept) 0.56 0.42 – 0.69 8.51 <0.001 58.00
videoclip [medium no] -0.02 -0.16 – 0.12 -0.30 0.762 58.00
videoclip [medium yes] -0.03 -0.17 – 0.10 -0.49 0.629 58.00
videoclip [deep no] -0.10 -0.23 – 0.04 -1.40 0.167 58.00
Random Effects
σ2 0.04
τ00 subj 0.03
ICC 0.45
N subj 16
Observations 64
Marginal R2 / Conditional R2 0.019 / 0.458
plot(allEffects(fam.glance1))

fam.glance2 <- lm(data = exp1.famtest.glanceoff,
             formula = scale(delta.look) ~ scale(shallow_yes) + scale(medium_no) + scale(medium_yes) + scale(deep_no))
tab_model(fam.glance2, show.stat=TRUE,show.df=TRUE)
  scale(delta look)
Predictors Estimates CI Statistic p df
(Intercept) -0.00 -0.51 – 0.51 -0.00 1.000 11.00
shallow yes -0.42 -1.16 – 0.32 -1.23 0.243 11.00
medium no -0.16 -0.76 – 0.44 -0.60 0.561 11.00
medium yes 0.21 -0.52 – 0.93 0.62 0.545 11.00
deep no -0.34 -0.95 – 0.27 -1.22 0.247 11.00
Observations 16
R2 / R2 adjusted 0.377 / 0.151
summary(fam.glance2)
## 
## Call:
## lm(formula = scale(delta.look) ~ scale(shallow_yes) + scale(medium_no) + 
##     scale(medium_yes) + scale(deep_no), data = exp1.famtest.glanceoff)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -1.476 -0.451 -0.215  0.597  1.567 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)
## (Intercept)        -2.41e-17   2.30e-01    0.00     1.00
## scale(shallow_yes) -4.15e-01   3.36e-01   -1.23     0.24
## scale(medium_no)   -1.64e-01   2.73e-01   -0.60     0.56
## scale(medium_yes)   2.05e-01   3.29e-01    0.62     0.55
## scale(deep_no)     -3.37e-01   2.76e-01   -1.22     0.25
## 
## Residual standard error: 0.92 on 11 degrees of freedom
## Multiple R-squared:  0.377,  Adjusted R-squared:  0.151 
## F-statistic: 1.66 on 4 and 11 DF,  p-value: 0.228
plot(allEffects(fam.glance2))

In Experiment 1, infants, on average, looked longer when the agent jumped deeper trenches for one goal than another, and then chose the other goal later at test. One question is how infants used the information in each of the 4 familiarization events, presented in a looping sequence over 6 familiarization trials, in order to draw this inference. Rather than comparing the relative acceptances and refusals of the agent across 3 different levels of peril (shallow, medium, and deep trenches), one alternative hypothesis is that infants selectively attended when the agent accepted and refused the same obstacle (medium trench) for the two goals, and used this ‘go-no-go’ heuristic to infer that the agent prefers the goal it jumped for, over the goal it refused to jump for.

On this hypothesis, infants should be less likely to glance away from events involving medium trenches (vs the other events), and those who looked away less (i.e. attended more) to the medium trench events should have exhibited larger violation-of-expectation effects at test. To test these predictions, naive coders chose a random 50% of videos from Experiment 1 and annotated the onset and offset times of each iteration of each event in each familiarization loop, ignoring interleaved blank screens, and then annotated the onset and offset of infants’ attention to each event iteration. In the plots and following analyses, the events are named shallow_yes and medium_yes when the agent willingly jumped a shallow or medium trench, and medium_no and deep_no when the agent refused to jump a medium or deep trench. For each infant, we calculated the number of each kind of event they saw. Then, we calculated the proportion of those events that infants looked away from. If an infant looked away from the screen for any portion of the event, we marked that event as one where they looked away. Otherwise, we marked that event as one where they looked For example, if an infant saw 5 deep_no events and glanced away from the screen for 1 of them, this produced a score of 0.2 for that event type, for that infant. We then averaged these proportions within infants across all 4 event types, to produce 4 different proportion glance-away scores per infant. These scores are plotted in Figure S3, are related to each other, and to infants’ looking preferences at test, in Figure S4.

Overall, infants were equally likely to glance away from the screen (vs attend for the entire duration) during the 4 events. See Table S1 for results of the linear mixed effects model (lmer formula: prop.glancedoff ~ videoclip + (1|subj)). Thus, infants did not attend selectively to the events where they had the opportunity to compare the agent’s acceptance and refusal of the medium trench towards the two goals. Instead, they were equally likely to glance away from all 4 types of events.

Table S1. Infants’ probability of glancing away from the 4 video clips from familiarization in Experiment 1

fam1 <- lmer(data = exp1.fam.bymovie,
               formula = proportion.on.total ~ videoclip + (1|subj))
summary(fam1)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: proportion.on.total ~ videoclip + (1 | subj)
##    Data: exp1.fam.bymovie
## 
## REML criterion at convergence: -149
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
## -3.044 -0.645  0.188  0.665  1.344 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  subj     (Intercept) 0.000997 0.0316  
##  Residual             0.003324 0.0577  
## Number of obs: 64, groups:  subj, 16
## 
## Fixed effects:
##                      Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)           0.92221    0.01643 51.74040   56.12   <2e-16 ***
## videoclipmedium_no   -0.02259    0.02038 45.00000   -1.11     0.27    
## videoclipmedium_yes  -0.01641    0.02038 45.00000   -0.80     0.43    
## videoclipshallow_yes -0.00738    0.02038 45.00000   -0.36     0.72    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) vdclpmdm_n vdclpmdm_y
## vidclpmdm_n -0.620                      
## vdclpmdm_ys -0.620  0.500               
## vdclpshllw_ -0.620  0.500      0.500
tab_model(fam1, show.stat=TRUE, show.df=TRUE)
  proportion on total
Predictors Estimates CI Statistic p df
(Intercept) 0.92 0.89 – 0.96 56.12 <0.001 58.00
videoclip [medium no] -0.02 -0.06 – 0.02 -1.11 0.272 58.00
videoclip [medium yes] -0.02 -0.06 – 0.02 -0.80 0.424 58.00
videoclip [shallow yes] -0.01 -0.05 – 0.03 -0.36 0.719 58.00
Random Effects
σ2 0.00
τ00 subj 0.00
ICC 0.23
N subj 16
Observations 64
Marginal R2 / Conditional R2 0.017 / 0.244
plot(allEffects(fam1))

We then tested the second prediction: that infants who glanced away from the medium trench events (i.e. those who missed the critical information for a go-no-go strategy) would also show smaller violation-of-expectation responses at test. To do this, we calculated infants’ looking preference at test (average duration looking when the agent chose the less valued goal, minus average duration looking when the agent chose the more valued goal), and asked whether variability in infants’ looking behavior towards each of the 4 events predicted variability in these looking preferences. We found that infants’ tendency to glance away from the events involving medium trenches, or towards any of the 4 events, did not predict the magnitude of their violation-of-expectation response. See Table S2 for full results (lm formula: delta.look ~ shallow_yes + medium_no + medium_yes + deep_no).

Together, these findings suggest that infants did not selectively attend to the videos with the same trench depth during familiarization in Experiment 1 (or selectively glance away from the other events), and that their looking towards these videos did not predict stronger inferences about which goal was more valuable. Therefore, it appears unlikely that infants as a group used a “go-no-go” heuristic on the agent’s actions over the medium trenches in order to infer which the agent preferred. To be clear, we are not suggesting that infants could never use such a strategy. Instead we are suggesting that this strategy does not appear to explain the results of Experiment 1 (based on this analysis), or the results of Experiments 2-3 (in principle, based on the experimental design, in which the agent always accepts and never refuses jumping actions)

Table S2. Infants’ violation of expectation responses at test, as predicted by their tendency to glance away from the 4 video clips from familiarization in Experiment 1. Dependent and independent variables were z-scored prior to entry into the model.

fam2 <- lm(data = exp1.famtest,
             formula = scale(delta.look) ~ scale(shallow_yes) + scale(medium_no) + scale(medium_yes) + scale(deep_no))
tab_model(fam2, show.stat=TRUE,show.df=TRUE)
  scale(delta look)
Predictors Estimates CI Statistic p df
(Intercept) 0.00 -0.49 – 0.49 0.00 1.000 11.00
shallow yes -0.01 -0.56 – 0.55 -0.03 0.973 11.00
medium no 0.12 -0.44 – 0.68 0.48 0.643 11.00
medium yes 0.33 -0.31 – 0.97 1.13 0.284 11.00
deep no 0.37 -0.19 – 0.94 1.45 0.176 11.00
Observations 16
R2 / R2 adjusted 0.419 / 0.208
summary(fam2)
## 
## Call:
## lm(formula = scale(delta.look) ~ scale(shallow_yes) + scale(medium_no) + 
##     scale(medium_yes) + scale(deep_no), data = exp1.famtest)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.8595 -0.3005  0.0388  0.3617  1.1282 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)
## (Intercept)         2.67e-16   2.22e-01    0.00     1.00
## scale(shallow_yes) -8.69e-03   2.53e-01   -0.03     0.97
## scale(medium_no)    1.20e-01   2.53e-01    0.48     0.64
## scale(medium_yes)   3.29e-01   2.92e-01    1.13     0.28
## scale(deep_no)      3.74e-01   2.59e-01    1.45     0.18
## 
## Residual standard error: 0.89 on 11 degrees of freedom
## Multiple R-squared:  0.419,  Adjusted R-squared:  0.208 
## F-statistic: 1.98 on 4 and 11 DF,  p-value: 0.166
plot(allEffects(fam2))

# sim <- powerCurve(extend(exp1.1, along="subj", n=500),
#                        along="subj", breaks = c(36, 40, 44, 48, 52, 56, 60, 64, 68), alpha = .05, seed = 123)
# plot(sim)
# print(sim)
# reliability <- wide %>% filter(reliability ==1) %>%
#   select(subj, sex, experiment, test1, test2, test3, test4) %>%
#   gather(trial, look, test1:test4) %>%
#   mutate(trialn = str_remove(trial, "test")) %>%
#   group_by(subj, trialn)
# write.csv(reliability, "risk_rel.csv")